CUDA: add CDNA3 MFMA support for flash attention MMA kernel (#19806 )

* CUDA: add CDNA3 MFMA support for flash attention MMA kernel Add MI300X (gfx942) MFMA tensor core flash attention using v_mfma_f32_16x16x16_f16 (FP16 in, FP32 accumulate). - Add FATTN_WARP_SIZE=64 for CDNA wavefront64 - Add CDNA config for head sizes 64, 80, 96, 112, 128 - Add FP16 MFMA intrinsic path in mma.cuh - Add manual V transpose load for MFMA register layout - Route CDNA to MMA for prompt processing, VEC for token generation - Fix Q loading and combine stride granularity for non-power-of-2 heads Benchmarks (Qwen2.5-1.5B Q4_K_M, MI300X): pp512 +7%, pp1024 +13%, pp2048 +23%, pp4096 +39% tg128 -10% (FA overhead, VEC used for both) All 2480 flash attention tests pass. Ref: https://github.com/ggml-org/llama.cpp/issues/17917 * address review: replace FATTN_WARP_SIZE with constexpr, improve dispatch - Replace #define FATTN_WARP_SIZE with constexpr int warp_size = ggml_cuda_get_physical_warp_size() in each device function - Use ne[1]*gqa_ratio threshold for MMA vs tile dispatch. Benchmarked crossover on MI300X @ d32768 with power-of-2 GQA models: hsk=64 (Llama 1B, gqa=4): MMA wins at eff >= 128 (+11%) hsk=128 (Llama 3B, gqa=4): MMA wins at eff >= 128 (+4%) Unified threshold: eff_nq >= 128 for all head sizes. - Remove VEC fallback; small batches fall through to tile kernel * Update ggml/src/ggml-cuda/fattn.cu * use ggml_cuda_info().devices warp_size instead of hardcoded check --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
server: Add pragma once to server-context.h (#19944 )
2026-07-17 09:55:58 +02:00 · 2026-02-27 19:37:26 +01:00 · 2026-02-27 18:28:36 +01:00 · 2026-02-28 00:44:42 +08:00 · 2026-02-27 14:42:24 +01:00 · 2026-02-27 18:15:09 +08:00
86 changed files with 2036 additions and 691 deletions
@@ -21,7 +21,7 @@ on:
 jobs:
  deploy:

-    runs-on: ubuntu-slim
+    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v6
@@ -2520,11 +2520,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ));
    add_opt(common_arg(
        {"-a", "--alias"}, "STRING",
-        "set alias for model name (to be used by REST API)",
+        "set model name aliases, comma-separated (to be used by API)",
        [](common_params & params, const std::string & value) {
-            params.model_alias = value;
+            for (auto & alias : string_split<std::string>(value, ',')) {
+                alias = string_strip(alias);
+                if (!alias.empty()) {
+                    params.model_alias.insert(alias);
+                }
+            }
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ALIAS"));
+    add_opt(common_arg(
+        {"--tags"}, "STRING",
+        "set model tags, comma-separated (informational, not used for routing)",
+        [](common_params & params, const std::string & value) {
+            for (auto & tag : string_split<std::string>(value, ',')) {
+                tag = string_strip(tag);
+                if (!tag.empty()) {
+                    params.model_tags.insert(tag);
+                }
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TAGS"));
    add_opt(common_arg(
        {"-m", "--model"}, "FNAME",
        ex == LLAMA_EXAMPLE_EXPORT_LORA
@@ -410,7 +410,8 @@ struct common_params {

    struct common_params_model model;

-    std::string model_alias          = ""; // model alias                                                   // NOLINT
+    std::set<std::string> model_alias;     // model aliases                                                 // NOLINT
+    std::set<std::string> model_tags;      // model tags (informational, not used for routing)              // NOLINT
    std::string hf_token             = ""; // HF token                                                      // NOLINT
    std::string prompt               = "";                                                                  // NOLINT
    std::string system_prompt        = "";                                                                  // NOLINT
@@ -721,6 +721,8 @@ value member_expression::execute_impl(context & ctx) {
        int64_t arr_size = 0;
        if (is_val<value_array>(object)) {
            arr_size = object->as_array().size();
+        } else if (is_val<value_string>(object)) {
+            arr_size = object->as_string().length();
        }

        if (is_stmt<slice_expression>(this->property)) {
@@ -116,7 +116,8 @@ class ModelBase:
                 split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
                 disable_mistral_community_chat_template: bool = False,
-                 sentence_transformers_dense_modules: bool = False):
+                 sentence_transformers_dense_modules: bool = False,
+                 fuse_gate_up_exps: bool = False):
        if type(self) is ModelBase or \
                type(self) is TextModel or \
                type(self) is MmprojModel:
@@ -135,6 +136,9 @@ class ModelBase:
        self.dry_run = dry_run
        self.remote_hf_model_id = remote_hf_model_id
        self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
+        self.fuse_gate_up_exps = fuse_gate_up_exps
+        self._gate_exp_buffer: dict[int, Tensor] = {}
+        self._up_exp_buffer: dict[int, Tensor] = {}
        self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
        self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
        self.metadata_override = metadata_override
@@ -512,8 +516,31 @@ class ModelBase:
        raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        del bid # unused
-        return [(self.map_tensor_name(name), data_torch)]
+        new_name = self.map_tensor_name(name)
+
+        # Handle gate/up expert tensor fusion if enabled
+        if self.fuse_gate_up_exps and bid is not None:
+            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid):
+                self._gate_exp_buffer[bid] = data_torch
+            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
+                self._up_exp_buffer[bid] = data_torch
+
+            # Check if both gate and up are buffered for this layer
+            if bid in self._gate_exp_buffer and bid in self._up_exp_buffer:
+                gate_data = self._gate_exp_buffer.pop(bid)
+                up_data = self._up_exp_buffer.pop(bid)
+                # gate/up shape: (n_expert, n_ff, n_embd), concatenate to (n_expert, n_ff*2, n_embd)
+                fused_data = torch.cat([gate_data, up_data], dim=1)
+                fused_name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_UP_EXP, bid)
+                logger.info(f"Fused gate_exps and up_exps for layer {bid}")
+                return [(fused_name, fused_data)]
+
+            # If we buffered a gate/up tensor, wait for the other
+            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_GATE_EXP, bid) or \
+               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
+                return []
+
+        return [(new_name, data_torch)]

    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
        del name, new_name, bid, n_dims  # unused
@@ -1148,6 +1175,9 @@ class TextModel(ModelBase):
        if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
            res = "jina-v2-de"
+        if chkhsh == "a023e9fdc5a11f034d3ef515b92350e56fb2af1f66c6b6811a4444ea9bf8763d":
+            # ref: https://huggingface.co/jinaai/jina-embeddings-v5-text-nano
+            res = "jina-v5-nano"
        if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d":
            # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
            res = "smaug-bpe"
@@ -6125,6 +6155,32 @@ class NeoBert(BertModel):
        yield from super().modify_tensors(data_torch, name, bid)


+@ModelBase.register("EuroBertModel", "JinaEmbeddingsV5Model")
+class EuroBertModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.EUROBERT
+
+    def set_vocab(self):
+        self.gguf_writer.add_add_bos_token(False)
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # EuroBert is bidirectional (encoder)
+        self.gguf_writer.add_causal_attention(False)
+
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+
+        self._try_set_pooling_type()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Strip "model." prefix from tensor names
+        if name.startswith("model."):
+            name = name[6:]
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
 class XLMRobertaModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT
@@ -11913,6 +11969,11 @@ def parse_args() -> argparse.Namespace:
              "Default these modules are not included.")
    )

+    parser.add_argument(
+        "--fuse-gate-up-exps", action="store_true",
+        help="Fuse gate_exps and up_exps tensors into a single gate_up_exps tensor for MoE models.",
+    )
+
    args = parser.parse_args()
    if not args.print_supported_models and args.model is None:
        parser.error("the following arguments are required: model")
@@ -12050,7 +12111,8 @@ def main() -> None:
                                     split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
                                     small_first_shard=args.no_tensor_first_split,
                                     remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template,
-                                     sentence_transformers_dense_modules=args.sentence_transformers_dense_modules
+                                     sentence_transformers_dense_modules=args.sentence_transformers_dense_modules,
+                                     fuse_gate_up_exps=args.fuse_gate_up_exps
                                     )

        if args.vocab_only:
@@ -107,6 +107,7 @@ models = [
    {"name": "jina-v2-en",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
    {"name": "jina-v2-es",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
    {"name": "jina-v2-de",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
+    {"name": "jina-v5-nano",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v5-text-nano", },
    {"name": "smaug-bpe",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
    {"name": "poro-chat",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
    {"name": "jina-v2-code",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
@@ -152,7 +152,9 @@ Commands and data are serialized using a custom binary protocol with:
 - **VM-specific**: Only works in virtual machines with virtio-gpu support
 - **Host dependency**: Requires properly configured host-side backend
 - **Latency**: Small overhead from VM escaping for each operation
-
+- **Shared-memory size**: with the `libkrun` hypervisor, the RAM + VRAM
+  addressable memory is limited to 64 GB. So the maximum GPU memory
+  will be `64GB - RAM`, regardless of the hardware VRAM size.

 * This work is pending upstream changes in the VirglRenderer
  project.
@@ -22,7 +22,7 @@

 **Llama.cpp + ZenDNN**

-The llama.cpp ZenDNN backend leverages AMD's optimized matrix multiplication primitives to accelerate inference on AMD CPUs. It utilizes ZenDNN's **LowOHA (Low Overhead Hardware Accelerated)** MatMul operator for efficient GEMM operations with minimal execution overhead, built-in weight caching, and direct access to backend libraries (AOCL BLIS, LibXSMM, OneDNN).
+The llama.cpp ZenDNN backend leverages AMD's optimized matrix multiplication primitives to accelerate inference on AMD CPUs. It utilizes ZenDNN's **LowOHA (Low Overhead Hardware Accelerated)** MatMul operator for efficient GEMM operations with minimal execution overhead, built-in weight caching, and direct access to backend libraries (AOCL DLP, LibXSMM, OneDNN).

 For more information about ZenDNN, visit: https://www.amd.com/en/developer/zendnn.html

@@ -32,7 +32,7 @@ For more information about ZenDNN, visit: https://www.amd.com/en/developer/zendn
 |:-------:|:-------:|:----------------------------------------------:|
 | Linux   | Support | Ubuntu 20.04, 22.04, 24.04                     |

-For the latest list of supported operating systems, see the [ZenDNN Supported OS](https://github.com/amd/ZenDNN/blob/zendnnl/README.md#15-supported-os).
+For the latest list of supported operating systems, see the [ZenDNN Supported OS](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/README.md#15-supported-os).

 ## Hardware

@@ -44,9 +44,9 @@ ZenDNN is optimized for AMD EPYC™ processors and AMD Ryzen™ processors based

 | CPU Family                    | Status  | Notes                              |
 |:-----------------------------:|:-------:|:----------------------------------:|
-| AMD EPYC™ 9005 Series (Turin)| Support | 5th Gen - Zen 5 architecture       |
-| AMD EPYC™ 9004 Series (Genoa)| Support | 4th Gen - Zen 4 architecture       |
-| AMD EPYC™ 7003 Series (Milan)| Support | 3rd Gen - Zen 3 architecture       |
+| AMD EPYC™ 9005 Series (Turin) | Support | 5th Gen - Zen 5 architecture       |
+| AMD EPYC™ 9004 Series (Genoa) | Support | 4th Gen - Zen 4 architecture       |
+| AMD EPYC™ 7003 Series (Milan) | Support | 3rd Gen - Zen 3 architecture       |
 | AMD Ryzen™ AI MAX (Strix Halo)| Support | High-performance mobile processors |

 *Notes:*
@@ -61,7 +61,7 @@ The ZenDNN backend currently accelerates **matrix multiplication (MUL_MAT)** ope

 | Operation    | Status  | Notes                                          |
 |:-------------|:-------:|:----------------------------------------------:|
-| MUL_MAT      |    ✓    | Accelerated via ZenDNN LowOHA MatMul           |
+| MUL_MAT      | Support | Accelerated via ZenDNN LowOHA MatMul           |

 *Note:* Since only MUL_MAT is accelerated, models will benefit most from ZenDNN when matrix multiplications dominate the computational workload (which is typical for transformer-based LLMs).

@@ -104,7 +104,6 @@ If you want to build ZenDNN yourself or use a specific version:
 # Clone ZenDNN repository
 git clone https://github.com/amd/ZenDNN.git
 cd ZenDNN
-git checkout zendnnl

 # Build and install (requires CMake >= 3.25)
 mkdir build && cd build
@@ -114,7 +113,7 @@ cmake --build . --target all

 Default installation path: `ZenDNN/build/install`

-**For detailed build instructions**, refer to the [ZenDNN README](https://github.com/amd/ZenDNN/blob/zendnnl/README.md).
+**For detailed build instructions**, refer to the [ZenDNN README](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/README.md).

 **Step 2: Build llama.cpp with custom ZenDNN path**

@@ -146,8 +145,7 @@ Run llama.cpp server with ZenDNN acceleration:

 ```sh
 # Set optimal configuration
-export OMP_NUM_THREADS=64  # Adjust to your CPU core count
-export ZENDNNL_MATMUL_ALGO=2  # Blocked AOCL BLIS for best performance
+export ZENDNNL_MATMUL_ALGO=1    # Blocked AOCL DLP algo for best performance

 # Start server
 ./build/bin/llama-server \
@@ -160,62 +158,26 @@ export ZENDNNL_MATMUL_ALGO=2  # Blocked AOCL BLIS for best performance
 Access the server at `http://localhost:8080`.

 **Performance tips**:
- Set `OMP_NUM_THREADS` to match your physical core count
- Use `ZENDNNL_MATMUL_ALGO=2` for optimal performance
+- Use `ZENDNNL_MATMUL_ALGO=1` for optimal performance
 - For NUMA systems: `numactl --cpunodebind=0 --membind=0 ./build/bin/llama-server ...`

 ## Environment Variable

-### Build Time
+For environment variables related to ZenDNN, refer to the [ZenDNN Environment Variables Documentation](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/docs/runtime_env.md).

-| Name               | Value                                 | Function                                    |
-|--------------------|---------------------------------------|---------------------------------------------|
-| GGML_ZENDNN        | ON/OFF                                | Enable ZenDNN backend support               |
-| ZENDNN_ROOT        | Path to ZenDNN installation           | Set ZenDNN installation directory           |
-| GGML_OPENMP        | ON/OFF (recommended: ON)              | Enable OpenMP for multi-threading           |
+### Performance Optimization

-### Runtime
-
-| Name                    | Value                    | Function                                                          |
-|-------------------------|--------------------------|-------------------------------------------------------------------|
-| OMP_NUM_THREADS         | Number (e.g., 64)        | Set number of OpenMP threads (recommended: physical core count)   |
-| ZENDNNL_MATMUL_ALGO     | 0-5                      | Select MatMul backend algorithm (see Performance Optimization)    |
-| ZENDNNL_PROFILE_LOG_LEVEL | 0-4                    | Profiling log level (0=disabled, 4=verbose)                       |
-| ZENDNNL_ENABLE_PROFILER | 0 or 1                   | Enable detailed profiling (1=enabled)                             |
-| ZENDNNL_API_LOG_LEVEL   | 0-4                      | API log level (0=disabled, 4=verbose)                             |
-
-**Example**:
+ZenDNN's LowOHA MatMul supports multiple backend algorithms. For **best performance**, use the **Blocked AOCL DLP** algorithm:

 ```sh
-export OMP_NUM_THREADS=64
-export ZENDNNL_MATMUL_ALGO=2  # Use Blocked AOCL BLIS for best performance
-./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Test" -n 100
+export ZENDNNL_MATMUL_ALGO=1    # Blocked AOCL DLP algo (recommended)
 ```

-## Performance Optimization
-
-### MatMul Algorithm Selection
-
-ZenDNN's LowOHA MatMul supports multiple backend algorithms. For **best performance**, use the **Blocked AOCL BLIS** algorithm:
-
-```sh
-export ZENDNNL_MATMUL_ALGO=2  # Blocked AOCL BLIS (recommended)
-```
-
-**Available algorithms**:
-
-| Value | Algorithm              | Description                                    |
-|:-----:|:-----------------------|:----------------------------------------------|
-| 0     | Dynamic Dispatch       | Automatic backend selection (default)         |
-| 1     | AOCL BLIS              | AOCL BLIS backend                             |
-| 2     | AOCL BLIS Blocked      | **Blocked AOCL BLIS (recommended)**           |
-| 3     | OneDNN                 | OneDNN backend                                |
-| 4     | OneDNN Blocked         | Blocked OneDNN                                |
-| 5     | LibXSMM                | LibXSMM backend                               |
+For more details on available algorithms, see the [ZenDNN MatMul Algorithm Documentation](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/docs/runtime_env.md#algorithm-details).

 ### Profiling and Debugging

-For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/zendnnl/docs/logging.md).
+For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/a18adf8c605fb5f5e52cefd7eda08a7b18febbaf/docs/logging.md).

 ## Known Issues

@@ -245,10 +207,9 @@ A: Currently, ZenDNN primarily supports FP32 and BF16 data types. Quantized mode

 A: Ensure:
 1. You're using an AMD EPYC or Ryzen processor (Zen 2 or newer)
-2. `OMP_NUM_THREADS` is set appropriately (physical core count)
-3. `ZENDNNL_MATMUL_ALGO=2` is set for best performance (Blocked AOCL BLIS)
-4. You're using a sufficiently large model (small models may not benefit as much)
-5. Enable profiling to verify ZenDNN MatMul is being called
+2. `ZENDNNL_MATMUL_ALGO=1` is set for best performance (Blocked AOCL DLP)
+3. You're using a sufficiently large model (small models may not benefit as much)
+4. Enable profiling to verify ZenDNN MatMul is being called

 ### **GitHub Contribution**:
 Please add the **[ZenDNN]** prefix/tag in issues/PRs titles to help the ZenDNN-team check/address them without delay.
@@ -141,27 +141,50 @@ static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_typ
 namespace ggml::cpu::amx {
 class extra_buffer_type : ggml::cpu::extra_buffer_type {
    bool supports_op(ggml_backend_dev_t, const struct ggml_tensor * op) override {
-        // handle only 2d gemm for now
-        auto is_contiguous_2d = [](const struct ggml_tensor * t) {
-            return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1;
-        };
-
-        if (op->op == GGML_OP_MUL_MAT && is_contiguous_2d(op->src[0]) &&  // src0 must be contiguous
-            is_contiguous_2d(op->src[1]) &&                               // src1 must be contiguous
-            op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_amx_buffer_type() &&
-            op->src[0]->ne[0] % (TILE_K * 2 * 32) == 0 && // TODO: not sure if correct (https://github.com/ggml-org/llama.cpp/pull/16315)
-            op->ne[0] % (TILE_N * 2) == 0 &&                              // out_features is 32x
-            (qtype_has_amx_kernels(op->src[0]->type) || (op->src[0]->type == GGML_TYPE_F16))) {
-            // src1 must be host buffer
-            if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
-                return false;
-            }
-            // src1 must be float32
-            if (op->src[1]->type == GGML_TYPE_F32) {
-                return true;
-            }
+        if (op->op != GGML_OP_MUL_MAT) {
+            return false;
        }
-        return false;
+        auto * src0 = op->src[0];
+        auto * src1 = op->src[1];
+
+        if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+            return false;
+        }
+        if (!src0->buffer || src0->buffer->buft != ggml_backend_amx_buffer_type()) {
+            return false;
+        }
+        if (src1->buffer && !ggml_backend_buft_is_host(src1->buffer->buft)) {
+            return false;
+        }
+        if (op->ne[0] % (TILE_N * 2)) {
+            return false;
+        }
+        int alignment;
+        switch (src0->type) {
+            case GGML_TYPE_Q4_0:
+            case GGML_TYPE_Q4_1:
+            case GGML_TYPE_Q8_0:
+                alignment = TILE_K;
+                break;
+            case GGML_TYPE_Q4_K:
+            case GGML_TYPE_Q5_K:
+            case GGML_TYPE_Q6_K:
+            case GGML_TYPE_IQ4_XS:
+                alignment = 256; // QK_K
+                break;
+            case GGML_TYPE_F16:
+                alignment = 16;
+                break;
+            default:
+                return false;
+        }
+        if (src0->ne[0] % alignment) {
+            return false;
+        }
+        if (src1->type != GGML_TYPE_F32) {
+            return false;
+        }
+        return true;
    }

    ggml::cpu::tensor_traits * get_tensor_traits(const struct ggml_tensor * op) override {
@@ -1,4 +1,3 @@
-
 #if defined(__GNUC__)
 #pragma GCC diagnostic ignored "-Wpedantic"
 #pragma GCC diagnostic ignored "-Wunused-local-typedefs"
@@ -202,35 +201,27 @@ struct tile_config_t{
 //    advanced-matrix-extensions-intrinsics-functions.html
 //

-#define TC_CONFIG_TILE(i, r, cb) tc.rows[i] = r; tc.colsb[i] = cb
-void ggml_tile_config_init(void) {
-    static thread_local bool is_first_time = true;
+inline void ggml_tile_config_init(void) {
+    static thread_local bool done = false;

-    if (!is_first_time) {
+    if (done) {
        return;
    }

-    static thread_local tile_config_t tc;
-    tile_config_t current_tc;
-    _tile_storeconfig(&current_tc);
+    alignas(64) tile_config_t tc = {};
+    tc.palette_id = 1;
+    tc.start_row = 0;
+    tc.rows[0] = 8;   tc.colsb[0] = 64;
+    tc.rows[1] = 8;   tc.colsb[1] = 64;
+    tc.rows[2] = 16;  tc.colsb[2] = 32;
+    tc.rows[3] = 16;  tc.colsb[3] = 32;
+    tc.rows[4] = 16;  tc.colsb[4] = 64;
+    tc.rows[5] = 16;  tc.colsb[5] = 64;
+    tc.rows[6] = 16;  tc.colsb[6] = 64;
+    tc.rows[7] = 16;  tc.colsb[7] = 64;

-    // load only when config changes
-    if (tc.palette_id == 0 || (memcmp(&current_tc.colsb, &tc.colsb, sizeof(uint16_t) * 8) != 0 &&
-                               memcmp(&current_tc.rows, &tc.rows, sizeof(uint8_t) * 8) != 0)) {
-        tc.palette_id = 1;
-        tc.start_row = 0;
-        TC_CONFIG_TILE(TMM0, 8, 64);
-        TC_CONFIG_TILE(TMM1, 8, 64);
-        TC_CONFIG_TILE(TMM2, 16, 32);
-        TC_CONFIG_TILE(TMM3, 16, 32);
-        TC_CONFIG_TILE(TMM4, 16, 64);
-        TC_CONFIG_TILE(TMM5, 16, 64);
-        TC_CONFIG_TILE(TMM6, 16, 64);
-        TC_CONFIG_TILE(TMM7, 16, 64);
-        _tile_loadconfig(&tc);
-    }
-
-    is_first_time = false;
+    _tile_loadconfig(&tc);
+    done = true;
 }

 // we need an extra 16 * 4B (TILE_N * int32_t) for each NB/KB block for compensation.
@@ -268,33 +259,6 @@ int get_row_size(int K) {
    return row_size;
 }

-// vectorized dtype conversion
-inline float FP16_TO_FP32(ggml_half val) {
-    __m256i v = _mm256_setr_epi16(
-        val, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0);
-    __m512 o = _mm512_cvtph_ps(v);
-    return _mm512_cvtss_f32(o);
-}
-
-inline __m512 FP16_TO_FP32_VEC(ggml_half val) {
-    __m256i v = _mm256_set1_epi16(val);
-    return _mm512_cvtph_ps(v);
-}
-
-// horizontal reduce
-inline float _mm512_reduce_max_ps(const __m512 x) {
-    __m512 v = x;
-    __m512 v1 = _mm512_shuffle_f32x4(v, v, 0x4E);
-    v = _mm512_max_ps(v, v1);
-    v1 = _mm512_shuffle_f32x4(v, v, 0xB1);
-    v = _mm512_max_ps(v, v1);
-    v1 = _mm512_shuffle_ps(v, v, 0x4E);
-    v = _mm512_max_ps(v, v1);
-    v1 = _mm512_shuffle_ps(v, v, 0xB1);
-    v = _mm512_max_ps(v, v1);
-    return _mm512_cvtss_f32(v);
-}
-
 // transpose utils
 #define SHUFFLE_EPI32(a, b, mask) \
    _mm256_castps_si256(_mm256_shuffle_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b), mask))
@@ -1370,9 +1334,9 @@ struct tinygemm_kernel_avx<float, ggml_fp16_t, float, BLOCK_M, BLOCK_N, BLOCK_K>

 #define LAUNCH_TINYGEMM_KERNEL_AVX(MB_SIZE, NB_SIZE)                                \
    tinygemm_kernel_avx<float, type, float, MB_SIZE, NB_SIZE, blck_size>::apply(    \
-        K, (const float *)src1->data + mb_start * K,                                \
-        (const type *)src0->data + nb_start * K,                                    \
-        (float *)dst->data + mb_start * ldc + nb_start, ldc);
+        K, (const float *)src1->data + src1_offset + mb_start * K,                  \
+        (const type *)src0->data + src0_offset + nb_start * K,                      \
+        (float *)dst->data + dst_offset + mb_start * ldc + nb_start, ldc)


 // re-organize in the format {NB, KB, TILE_SIZE}:
@@ -2019,11 +1983,11 @@ struct tinygemm_kernel_vnni<block_q8_K, block_iq4_xs, float, BLOCK_M, BLOCK_N, B
    }
 };

-#define LAUNCH_TINYGEMM_KERNEL_VNNI(NB_SIZE)                                         \
-    tinygemm_kernel_vnni<vec_dot_type, type, float, 1, NB_SIZE, blck_size>::apply(   \
-        KB, (const char *)wdata + 0 * row_size_A,                                    \
-        (const char *)src0->data + PACKED_INDEX(nb * kTilesN, 0, KB, TILE_SIZE),     \
-        (float *) dst->data + 0 * N + nb_start, ldc)
+#define LAUNCH_TINYGEMM_KERNEL_VNNI(NB_SIZE)                                                   \
+    tinygemm_kernel_vnni<vec_dot_type, type, float, 1, NB_SIZE, blck_size>::apply(             \
+        KB, wdata_batch,                                                                       \
+        (const char *)src0->data + src0_offset + PACKED_INDEX(nb * kTilesN, 0, KB, TILE_SIZE), \
+        (float *) dst->data + dst_offset + nb_start, ldc)

 template <typename TA, typename TB, typename TC, int BLOCK_K,
          typename std::enable_if<!is_type_qkk<TB>::value, int>::type = 0>
@@ -2079,7 +2043,7 @@ void tinygemm_kernel_amx(int M, int N, int KB, const void * RESTRICT _A, const v
        _tile_stored(TMM5, Tile5(C_pre), TILE_N * sizeof(int32_t));

        if (need_unpack) {
-            unpack_B<TB>(Tile1, B_blk0);
+            unpack_B<TB>(Tile1, B_blk1);
            _tile_loadd(TMM1, Tile1, TILE_N * VNNI_BLK);
        } else {
            _tile_loadd(TMM1, B_blk1, TILE_N * VNNI_BLK);
@@ -2336,6 +2300,13 @@ void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * d
    });
 }

+// ne2 is passed explicitly to help compiler optimize repeated calls
+inline int64_t ggml_batch_offset(const ggml_tensor * t, int64_t batch_idx, int64_t ne2) {
+    const int64_t i2 = batch_idx % ne2;
+    const int64_t i3 = batch_idx / ne2;
+    return i3 * t->nb[3] + i2 * t->nb[2];
+}
+
 size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst) {
    struct ggml_tensor * src0 = dst->src[0];

@@ -2348,12 +2319,13 @@ size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst) {

    const int M = dst->ne[1];
    const int K = src0->ne[0];
+    const int64_t n_batch = dst->ne[2] * dst->ne[3];

    size_t desired_wsize = 0;

    GGML_DISPATCH_QTYPES(TYPE, [&] {
        const size_t row_size_A = K / blck_size * sizeof(vec_dot_type);
-        desired_wsize = M * row_size_A;
+        desired_wsize = n_batch * M * row_size_A;
    });

    return desired_wsize;
@@ -2365,7 +2337,7 @@ size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst) {
 // src1: input  in shape of {M, K}, float32
 // dst:  output in shape of {M, N}, float32
 //
-// the function performs: dst = src1 @ src0.T
+// the function performs: dst = src1 @ src0.T for each batch
 //
 void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_tensor * dst) {
    struct ggml_tensor * src0 = dst->src[0];
@@ -2382,17 +2354,26 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te
    const int K = src0->ne[0];
    const int ldc = dst->nb[1] / dst->nb[0];

+    const int64_t ne2 = dst->ne[2];
+    const int64_t n_batch = ne2 * dst->ne[3];
+
    if (is_floating_type) {
        constexpr int BLOCK_M = 4;
        constexpr int BLOCK_N = 6;
        const int MB = div_up(M, BLOCK_M);
        const int NB = div_up(N, BLOCK_N);

-        parallel_for_ggml(params, MB * NB, [&](int begin, int end) {
+        parallel_for_ggml(params, n_batch * MB * NB, [&](int begin, int end) {
            GGML_DISPATCH_FLOATING_TYPES(TYPE, [&] {
                for (int i = begin; i < end; ++i) {
-                    int mb = i / NB;
-                    int nb = i % NB;
+                    int batch_idx = i / (MB * NB);
+                    int remaining = i % (MB * NB);
+                    int mb = remaining / NB;
+                    int nb = remaining % NB;
+
+                    int64_t src0_offset = ggml_batch_offset(src0, batch_idx, ne2);
+                    int64_t src1_offset = ggml_batch_offset(src1, batch_idx, ne2);
+                    int64_t dst_offset  = ggml_batch_offset(dst,  batch_idx, ne2);

                    int mb_start = mb * BLOCK_M;
                    int mb_size = std::min(BLOCK_M, M - mb_start);
@@ -2424,10 +2405,10 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te
    void * wdata = params->wdata;

    //TODO: performance improvement: merge quant A
-    if (params->ith == 0) {
+ // if (params->ith == 0) {
        GGML_DISPATCH_QTYPES(TYPE, [&] {
            const size_t row_size_A = K / blck_size * sizeof(vec_dot_type);
-            const size_t desired_wsize = M * row_size_A;
+            const size_t desired_wsize = n_batch * M * row_size_A;
            if (params->wsize < desired_wsize) {
                GGML_ABORT("insufficient work space size");
            }
@@ -2436,12 +2417,19 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te
            // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size
            GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size);

-            const float * A_data = static_cast<const float *>(src1->data);
-            for (int m = 0; m < M; ++m) {
-                from_float<vec_dot_type>(A_data + m * K, (char *)wdata + m * row_size_A, K);
-            }
+            parallel_for_ggml(params, n_batch, [&](int begin, int end) {
+                for (int batch_idx = begin; batch_idx < end; ++batch_idx) {
+                    int64_t src1_offset = ggml_batch_offset(src1, batch_idx, ne2);
+                    const float * A_data = (const float *)((const char *)src1->data + src1_offset);
+                    char * wdata_batch = (char *)wdata + batch_idx * M * row_size_A;
+
+                    for (int m = 0; m < M; ++m) {
+                        from_float<vec_dot_type>(A_data + m * K, wdata_batch + m * row_size_A, K);
+                    }
+                }
+            });
        });
-    }
+ // }

    ggml_barrier(params->threadpool);

@@ -2451,13 +2439,19 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te
        constexpr int BLOCK_N = TILE_N * kTilesN;
        const int NB = div_up(N, BLOCK_N);

-        parallel_for_ggml(params, NB, [&](int begin, int end) {
+        parallel_for_ggml(params, n_batch * NB, [&](int begin, int end) {
            GGML_DISPATCH_QTYPES(TYPE, [&] {
                const int KB = K / blck_size;
                const int TILE_SIZE = get_tile_size<type>();
                const int row_size_A = KB * sizeof(vec_dot_type);
                for (int i = begin; i < end; ++i) {
-                    int nb = i;
+                    int batch_idx = i / NB;
+                    int nb = i % NB;
+
+                    int64_t src0_offset = ggml_batch_offset(src0, batch_idx, ne2);
+                    int64_t dst_offset  = ggml_batch_offset(dst,  batch_idx, ne2);
+                    const char * wdata_batch = (const char *)wdata + batch_idx * row_size_A;
+
                    int nb_start = nb * BLOCK_N;
                    int nb_size = std::min(BLOCK_N, N - nb_start); // 32, 64, 96

@@ -2481,7 +2475,7 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te
    const int MB = div_up(M, BLOCK_M);
    const int NB = div_up(N, BLOCK_N);

-    parallel_for_ggml(params, MB * NB, [&](int begin, int end) {
+    parallel_for_ggml(params, n_batch * MB * NB, [&](int begin, int end) {
        // init tile config for each thread
        ggml_tile_config_init();

@@ -2491,8 +2485,14 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te
            const int row_size_A = KB * sizeof(vec_dot_type);

            for (int i = begin; i < end; ++i) {
-                int mb = i / NB;
-                int nb = i % NB;
+                int batch_idx = i / (MB * NB);
+                int remaining = i % (MB * NB);
+                int mb = remaining / NB;
+                int nb = remaining % NB;
+
+                int64_t src0_offset = ggml_batch_offset(src0, batch_idx, ne2);
+                int64_t dst_offset  = ggml_batch_offset(dst,  batch_idx, ne2);
+                const char * wdata_batch = (const char *)wdata + batch_idx * M * row_size_A;

                int mb_start = mb * BLOCK_M;
                int mb_size = std::min(BLOCK_M, M - mb_start);
@@ -2501,9 +2501,9 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te

                tinygemm_kernel_amx<vec_dot_type, type, float, blck_size>(
                    mb_size, nb_size, KB,
-                    (const char *)wdata + mb_start * row_size_A,
-                    (const char *)src0->data + PACKED_INDEX(nb * 2, 0, KB, TILE_SIZE),
-                    (float *) dst->data + mb_start * N + nb_start, ldc);
+                    wdata_batch + mb_start * row_size_A,
+                    (const char *)src0->data + src0_offset + PACKED_INDEX(nb * 2, 0, KB, TILE_SIZE),
+                    (float *) dst->data + dst_offset + mb_start * N + nb_start, ldc);
            }
        });
    });
@@ -48,6 +48,8 @@
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
+#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0
+#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
@@ -62,6 +64,8 @@
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
+#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0
+#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
@@ -69,8 +73,10 @@
 #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
+#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
+#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
 // repack.cpp
@@ -84,6 +90,7 @@
 #define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
+#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
@@ -94,6 +101,7 @@
 #define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
+#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #elif defined(__POWERPC__) || defined(__powerpc__)
@@ -120,6 +128,8 @@
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
+#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0
+#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
@@ -134,6 +144,8 @@
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
+#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0
+#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #elif defined(__loongarch64)
@@ -160,6 +172,8 @@
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
+#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0
+#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
@@ -174,6 +188,8 @@
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
+#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0
+#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #elif defined(__riscv)
@@ -201,6 +217,8 @@
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
+#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0
+#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
@@ -214,6 +232,8 @@
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
+#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0
+#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #elif defined(__s390x__)
@@ -246,6 +266,8 @@
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
+#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0
+#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
@@ -260,6 +282,8 @@
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
+#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0
+#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #elif defined(__wasm__)
@@ -294,6 +318,8 @@
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
+#define ggml_gemv_mxfp4_4x4_q8_0_generic ggml_gemv_mxfp4_4x4_q8_0
+#define ggml_gemv_mxfp4_8x8_q8_0_generic ggml_gemv_mxfp4_8x8_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
 #define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
@@ -308,6 +334,8 @@
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
+#define ggml_gemm_mxfp4_4x4_q8_0_generic ggml_gemm_mxfp4_4x4_q8_0
+#define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #endif
@@ -498,6 +498,81 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
    ggml_gemv_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }

+void ggml_gemv_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 4;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    const int8x16_t kvalues = vld1q_s8(kvalues_mxfp4);
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    float * res_ptr = s;
+
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_mxfp4x4 * b_ptr = (const block_mxfp4x4 *) vx + (x * nb);
+
+        float32x4_t sumf = vdupq_n_f32(0);
+        for (int l = 0; l < nb; l++) {
+            uint8x16_t b_0 = vld1q_u8(b_ptr[l].qs + 0);
+            uint8x16_t b_1 = vld1q_u8(b_ptr[l].qs + 16);
+            uint8x16_t b_2 = vld1q_u8(b_ptr[l].qs + 32);
+            uint8x16_t b_3 = vld1q_u8(b_ptr[l].qs + 48);
+
+            int8x16_t b_0_hi = vqtbl1q_s8(kvalues, b_0 >> 4);
+            int8x16_t b_0_lo = vqtbl1q_s8(kvalues, b_0 & 0x0F);
+            int8x16_t b_1_hi = vqtbl1q_s8(kvalues, b_1 >> 4);
+            int8x16_t b_1_lo = vqtbl1q_s8(kvalues, b_1 & 0x0F);
+            int8x16_t b_2_hi = vqtbl1q_s8(kvalues, b_2 >> 4);
+            int8x16_t b_2_lo = vqtbl1q_s8(kvalues, b_2 & 0x0F);
+            int8x16_t b_3_hi = vqtbl1q_s8(kvalues, b_3 >> 4);
+            int8x16_t b_3_lo = vqtbl1q_s8(kvalues, b_3 & 0x0F);
+
+            int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 0);
+            int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16);
+
+            int32x4_t sumi = vdupq_n_s32(0);
+            sumi = vdotq_laneq_s32(sumi, b_0_lo, a_0, 0);
+            sumi = vdotq_laneq_s32(sumi, b_0_hi, a_1, 0);
+            sumi = vdotq_laneq_s32(sumi, b_1_lo, a_0, 1);
+            sumi = vdotq_laneq_s32(sumi, b_1_hi, a_1, 1);
+            sumi = vdotq_laneq_s32(sumi, b_2_lo, a_0, 2);
+            sumi = vdotq_laneq_s32(sumi, b_2_hi, a_1, 2);
+            sumi = vdotq_laneq_s32(sumi, b_3_lo, a_0, 3);
+            sumi = vdotq_laneq_s32(sumi, b_3_hi, a_1, 3);
+
+            float32x4_t a_d = vcvt_f32_f16(vld1_dup_f16((const float16_t *)&a_ptr[l].d));
+            float32x4_t b_d = {
+                GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[0]),
+                GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[1]),
+                GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[2]),
+                GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[3]),
+            };
+            float32x4_t d = a_d * b_d;
+
+            sumf = vmlaq_f32(sumf, d, vcvtq_f32_s32(sumi));
+        }
+
+        vst1q_f32(res_ptr + x * 4, sumf);
+    }
+    return;
+#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
+    ggml_gemv_mxfp4_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemv_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    constexpr int qk = QK_K;
    const int     nb = n / qk;
@@ -3164,6 +3239,87 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
    ggml_gemm_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }

+void ggml_gemm_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 4;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    const int8x16_t kvalues = vld1q_s8(kvalues_mxfp4);
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_mxfp4x4 * b_ptr = (const block_mxfp4x4 *) vx + (x * nb);
+
+            float32x4_t sumf[4];
+            for (int m = 0; m < 4; m++) {
+                sumf[m] = vdupq_n_f32(0);
+            }
+
+            for (int l = 0; l < nb; l++) {
+                float32x4_t a_d = vcvt_f32_f16(vld1_f16((const float16_t *)a_ptr[l].d));
+                float32x4_t b_d = {
+                    GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[0]),
+                    GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[1]),
+                    GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[2]),
+                    GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[3]),
+                };
+
+                int32x4_t sumi_0 = vdupq_n_s32(0);
+                int32x4_t sumi_1 = vdupq_n_s32(0);
+                int32x4_t sumi_2 = vdupq_n_s32(0);
+                int32x4_t sumi_3 = vdupq_n_s32(0);
+
+                for (int k = 0; k < 4; k++) {
+                    int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 16 * k + 0);
+                    int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16 * k + 64);
+
+                    uint8x16_t b = vld1q_u8(b_ptr[l].qs + 16 * k);
+                    int8x16_t b_hi = vqtbl1q_s8(kvalues, b >> 4);
+                    int8x16_t b_lo = vqtbl1q_s8(kvalues, b & 0xF);
+
+                    sumi_0 = vdotq_laneq_s32(sumi_0, b_lo, a_0, 0);
+                    sumi_1 = vdotq_laneq_s32(sumi_1, b_lo, a_0, 1);
+                    sumi_2 = vdotq_laneq_s32(sumi_2, b_lo, a_0, 2);
+                    sumi_3 = vdotq_laneq_s32(sumi_3, b_lo, a_0, 3);
+                    sumi_0 = vdotq_laneq_s32(sumi_0, b_hi, a_1, 0);
+                    sumi_1 = vdotq_laneq_s32(sumi_1, b_hi, a_1, 1);
+                    sumi_2 = vdotq_laneq_s32(sumi_2, b_hi, a_1, 2);
+                    sumi_3 = vdotq_laneq_s32(sumi_3, b_hi, a_1, 3);
+                }
+
+                sumf[0] = vmlaq_f32(sumf[0], vmulq_laneq_f32(b_d, a_d, 0), vcvtq_f32_s32(sumi_0));
+                sumf[1] = vmlaq_f32(sumf[1], vmulq_laneq_f32(b_d, a_d, 1), vcvtq_f32_s32(sumi_1));
+                sumf[2] = vmlaq_f32(sumf[2], vmulq_laneq_f32(b_d, a_d, 2), vcvtq_f32_s32(sumi_2));
+                sumf[3] = vmlaq_f32(sumf[3], vmulq_laneq_f32(b_d, a_d, 3), vcvtq_f32_s32(sumi_3));
+            }
+
+            for (int m = 0; m < 4; m++) {
+                vst1q_f32(s + (y * 4 + m) * bs + x * 4, sumf[m]);
+            }
+        }
+    }
+    return;
+#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
+    ggml_gemm_mxfp4_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemm_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    constexpr int qk = QK_K;
    const int     nb = n / qk;
@@ -522,7 +522,8 @@ template<typename block_tx8>
 static void gemv_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc, __m256i signextendlut) {
    static_assert(
            std::is_same_v<block_tx8, block_q4_0x8> ||
-            std::is_same_v<block_tx8, block_iq4_nlx8>,
+            std::is_same_v<block_tx8, block_iq4_nlx8> ||
+            std::is_same_v<block_tx8, block_mxfp4x8>,
            "Unsupported block type");

    const int qk = QK8_0;
@@ -580,6 +581,18 @@ static void gemv_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
                        std::is_same_v<block_tx8, block_q4_0x8> ||
                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
                    col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask);
+                } else if constexpr (std::is_same_v<block_tx8, block_mxfp4x8>) {
+                    // Load 8 E8M0 exponents and convert to float via LUT
+                    // Rearranged to match changemask order: 0,4,1,5,2,6,3,7
+                    col_scale_f32 = _mm256_set_ps(
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[7]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[3]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[6]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[2]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[5]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[1]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[4]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[0]));
                }

                // Load and convert to FP32 scale from block_q8_0
@@ -628,7 +641,8 @@ template<typename block_tx8>
 static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc, __m256i signextendlut) {
    static_assert(
            std::is_same_v<block_tx8, block_q4_0x8> ||
-            std::is_same_v<block_tx8, block_iq4_nlx8>,
+            std::is_same_v<block_tx8, block_iq4_nlx8> ||
+            std::is_same_v<block_tx8, block_mxfp4x8>,
            "Unsupported block type");

    const int qk = QK8_0;
@@ -749,6 +763,25 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
                        std::is_same_v<block_tx8, block_q4_0x8> ||
                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
                    col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d);
+                } else if constexpr (std::is_same_v<block_tx8, block_mxfp4x8>) {
+                    //TODO: simd-ify
+                    col_scale_f32 = _mm512_set_ps(
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[7]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[6]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[5]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[4]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[3]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[2]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[1]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[0]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[7]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[6]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[5]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[4]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[3]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[2]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[1]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[0]));
                }

                // Process LHS in pairs of rows
@@ -941,6 +974,25 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
                        std::is_same_v<block_tx8, block_q4_0x8> ||
                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
                    col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d);
+                } else if constexpr (std::is_same_v<block_tx8, block_mxfp4x8>) {
+                    //TODO: simd-ify
+                    col_scale_f32 = _mm512_set_ps(
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[7]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[6]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[5]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[4]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[3]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[2]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[1]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_1[b].e[0]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[7]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[6]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[5]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[4]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[3]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[2]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[1]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr_0[b].e[0]));
                }

                // Load the four blocks of quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
@@ -1123,6 +1175,16 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
                        std::is_same_v<block_tx8, block_q4_0x8> ||
                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
                    col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d);
+                } else if constexpr (std::is_same_v<block_tx8, block_mxfp4x8>) {
+                    col_scale_f32 = _mm256_set_ps(
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[7]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[6]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[5]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[4]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[3]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[2]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[1]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[0]));
                }

                // Process LHS in groups of four
@@ -1283,6 +1345,16 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
                        std::is_same_v<block_tx8, block_q4_0x8> ||
                        std::is_same_v<block_tx8, block_iq4_nlx8>) {
                    col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d);
+                } else if constexpr (std::is_same_v<block_tx8, block_mxfp4x8>) {
+                    col_scale_f32 = _mm256_set_ps(
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[7]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[6]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[5]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[4]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[3]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[2]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[1]),
+                        GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[b].e[0]));
                }

                // Load the four blocks of quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3
@@ -1625,6 +1697,19 @@ void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
    ggml_gemv_iq4_nl_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }

+void ggml_gemv_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+#if defined(__AVX2__)
+    __m256i signextendlut = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i*)kvalues_mxfp4));
+    signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
+
+    gemv_q4_b32_8x8_q8_0_lut_avx<block_mxfp4x8>(n, s, bs, vx, vy, nr, nc, signextendlut);
+
+    return;
+#endif
+
+    ggml_gemv_mxfp4_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK_K;
    const int nb = n / qk;
@@ -3423,6 +3508,21 @@ void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
    ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }

+void ggml_gemm_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+#if defined(__AVX2__) || defined(__AVX512F__)
+    {
+        __m256i signextendlut = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i*)kvalues_mxfp4));
+        signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0);
+
+        gemm_q4_b32_8x8_q8_0_lut_avx<block_mxfp4x8>(n, s, bs, vx, vy, nr, nc, signextendlut);
+
+        return;
+    }
+#endif // defined(__AVX2__) || defined(__AVX512F__)
+
+    ggml_gemm_mxfp4_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
    const int qk = QK_K;
    const int nb = n / qk;
@@ -1098,6 +1098,82 @@ void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
    }
 }

+void ggml_gemv_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 4;
+
+    assert(nr == 1);
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(bs);
+    UNUSED(nr);
+
+    float sumf[4];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_mxfp4x4 * b_ptr = (const block_mxfp4x4 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                        const int v1 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
+                    }
+                    sumf[j] += sumi * GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+}
+
+void ggml_gemv_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert(nr == 1);
+    assert(n % qk == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(bs);
+    UNUSED(nr);
+
+    float sumf[8];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_mxfp4x8 * b_ptr = (const block_mxfp4x8 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                        const int v1 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
+                    }
+                    sumf[j] += sumi * GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+}
+
 void ggml_gemv_q8_0_4x4_q8_0_generic(int                        n,
                                     float * GGML_RESTRICT      s,
                                     size_t                     bs,
@@ -1726,6 +1802,94 @@ void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
    }
 }

+void ggml_gemm_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 4;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    float sumf[4][4];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_mxfp4x4 * b_ptr = (const block_mxfp4x4 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                                const int v1 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4]));
+                            }
+                            sumf[m][j] += sumi * GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+}
+
+void ggml_gemm_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    float sumf[4][8];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_mxfp4x8 * b_ptr = (const block_mxfp4x8 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                                const int v1 = kvalues_mxfp4[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4]));
+                            }
+                            sumf[m][j] += sumi * GGML_CPU_E8M0_TO_FP32_HALF(b_ptr[l].e[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+}
+
 void ggml_gemm_q8_0_4x4_q8_0_generic(int                        n,
                                     float * GGML_RESTRICT      s,
                                     size_t                     bs,
@@ -2510,6 +2674,121 @@ static int repack_iq4_nl_to_iq4_nl_8_bl(struct ggml_tensor * t, int interleave_b
    GGML_UNUSED(data_size);
 }

+
+static block_mxfp4x4 make_block_mxfp4x4(block_mxfp4 * in, unsigned int blck_size_interleave) {
+    block_mxfp4x4 out;
+
+    for (int i = 0; i < 4; i++) {
+        out.e[i] = in[i].e;
+    }
+
+    const int end = QK_MXFP4 * 2 / blck_size_interleave;
+
+    if (blck_size_interleave == 4) {
+        for (int i = 0; i < end; ++i) {
+            int src_id = i % 4;
+            int src_offset = (i / 4) * blck_size_interleave;
+            int dst_offset = i * blck_size_interleave;
+
+            memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint32_t));
+        }
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    return out;
+}
+
+static int repack_mxfp4_to_mxfp4_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_MXFP4);
+    GGML_ASSERT(interleave_block == 4);
+
+    const block_mxfp4   * src = (const block_mxfp4   *)data;
+          block_mxfp4x4 * dst = (      block_mxfp4x4 *)t->data;
+
+    block_mxfp4 dst_tmp[4];
+
+    int nrow = ggml_nrows(t);
+    int nrows_interleaved = 4;
+    int nblocks = t->ne[0] / QK_MXFP4;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_mxfp4));
+
+    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            *dst++ = make_block_mxfp4x4(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
+static block_mxfp4x8 make_block_mxfp4x8(block_mxfp4 * in, unsigned int blck_size_interleave) {
+    block_mxfp4x8 out;
+
+    for (int i = 0; i < 8; i++) {
+        out.e[i] = in[i].e;
+    }
+
+    const int end = QK_MXFP4 * 4 / blck_size_interleave;
+
+    if (blck_size_interleave == 8) {
+        for (int i = 0; i < end; ++i) {
+            int src_id = i % 8;
+            int src_offset = (i / 8) * blck_size_interleave;
+            int dst_offset = i * blck_size_interleave;
+
+            memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t));
+        }
+    } else {
+        GGML_ASSERT(false);
+    }
+
+    return out;
+}
+
+static int repack_mxfp4_to_mxfp4_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
+    GGML_ASSERT(t->type == GGML_TYPE_MXFP4);
+    GGML_ASSERT(interleave_block == 8);
+
+    const block_mxfp4   * src = (const block_mxfp4   *)data;
+          block_mxfp4x8 * dst = (      block_mxfp4x8 *)t->data;
+
+    block_mxfp4 dst_tmp[8];
+
+    int nrow = ggml_nrows(t);
+    int nrows_interleaved = 8;
+    int nblocks = t->ne[0] / QK_MXFP4;
+
+    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_mxfp4));
+
+    if (t->ne[1] % nrows_interleaved != 0) {
+        return -1;
+    }
+
+    for (int b = 0; b < nrow; b += nrows_interleaved) {
+        for (int64_t x = 0; x < nblocks; x++) {
+            for (int i = 0; i < nrows_interleaved; i++) {
+                dst_tmp[i] = src[x + i * nblocks];
+            }
+            *dst++ = make_block_mxfp4x8(dst_tmp, interleave_block);
+        }
+        src += nrows_interleaved * nblocks;
+    }
+    return 0;
+
+    GGML_UNUSED(data_size);
+}
+
 namespace ggml::cpu::repack {
 // repack
 template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
@@ -2569,6 +2848,14 @@ template <> int repack<block_iq4_nl, 8, 8>(struct ggml_tensor * t, const void *
    return repack_iq4_nl_to_iq4_nl_8_bl(t, 8, data, data_size);
 }

+template <> int repack<block_mxfp4, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_mxfp4_to_mxfp4_4_bl(t, 4, data, data_size);
+}
+
+template <> int repack<block_mxfp4, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_mxfp4_to_mxfp4_8_bl(t, 8, data, data_size);
+}
+
 template <> int repack<block_q8_0, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
    return repack_q8_0_to_q8_0_4_bl(t, 4, data, data_size);
 }
@@ -2636,6 +2923,14 @@ template <> void gemv<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size
    ggml_gemv_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }

+template <> void gemv<block_mxfp4, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_mxfp4_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+template <> void gemv<block_mxfp4, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_mxfp4_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemv<block_q8_0, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemv_q8_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
@@ -2703,6 +2998,14 @@ template <> void gemm<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size
    ggml_gemm_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }

+template <> void gemm<block_mxfp4, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_mxfp4_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
+template <> void gemm<block_mxfp4, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_mxfp4_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemm<block_q8_0, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
    ggml_gemm_q8_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
@@ -3111,6 +3414,10 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
    static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
    static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0> iq4_nl_8x8_q8_0;

+    // instance for MXFP4
+    static const ggml::cpu::repack::tensor_traits<block_mxfp4, 4, 4, GGML_TYPE_Q8_0> mxfp4_4x4_q8_0;
+    static const ggml::cpu::repack::tensor_traits<block_mxfp4, 8, 8, GGML_TYPE_Q8_0> mxfp4_8x8_q8_0;
+
    // instance for Q8_0
    static const ggml::cpu::repack::tensor_traits<block_q8_0, 4, 4, GGML_TYPE_Q8_0> q8_0_4x4_q8_0;
    static const ggml::cpu::repack::tensor_traits<block_q8_0, 8, 4, GGML_TYPE_Q8_0> q8_0_4x8_q8_0;
@@ -3187,6 +3494,17 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
                return &iq4_nl_4x4_q8_0;
            }
        }
+    } else if (cur->type == GGML_TYPE_MXFP4) {
+        if (ggml_cpu_has_avx2()) {
+            if (cur->ne[1] % 8 == 0) {
+                return &mxfp4_8x8_q8_0;
+            }
+        }
+        if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
+            if (cur->ne[1] % 4 == 0) {
+                return &mxfp4_4x4_q8_0;
+            }
+        }
    } else if (cur->type == GGML_TYPE_Q8_0) {
        if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
            if (cur->ne[1] % 4 == 0) {
@@ -97,6 +97,19 @@ struct block_iq4_nlx8 {

 static_assert(sizeof(block_iq4_nlx8) == 8 * sizeof(ggml_half) + QK4_NL * 4, "wrong iq4_nlx8 block size/padding");

+struct block_mxfp4x4 {
+    uint8_t e[4];
+    uint8_t qs[QK_MXFP4 * 2];
+};
+static_assert(sizeof(block_mxfp4x4) == 4 + QK_MXFP4 * 2, "wrong mxfp4x4 block size/padding");
+
+struct block_mxfp4x8 {
+    uint8_t e[8];
+    uint8_t qs[QK_MXFP4 * 4];
+};
+static_assert(sizeof(block_mxfp4x8) == 8 + QK_MXFP4 * 4, "wrong mxfp4x8 block size/padding");
+
+
 #if defined(__cplusplus)
 extern "C" {
 #endif
@@ -117,6 +130,8 @@ void ggml_gemv_q6_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemv_q6_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -129,6 +144,8 @@ void ggml_gemm_q6_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemm_q6_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -151,6 +168,8 @@ void ggml_gemv_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemv_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -163,6 +182,8 @@ void ggml_gemm_q6_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemm_q6_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -111,6 +111,44 @@ static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_co
    return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
 }

+static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_cdna(const int DKQ, const int DV, const int ncols) {
+    // Conservative configs for CDNA (MI100+): 64KB LDS, wavefront64, nstages=1 (no cp.async).
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64,  8, 128, 2, 128,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 16, 128, 2,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 32, 128, 2,  64,  32,  32,  32, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64,  64, 64, 256, 2,  64,  32,  32,  32, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80,  8, 128, 2, 128,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 16, 128, 2,  64,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 32, 128, 2,  64,  40,  40,  40, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80,  80, 64, 256, 2,  64,  40,  40,  40, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96,  8, 128, 2, 128,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 16, 128, 2,  64,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 32, 128, 2,  64,  48,  48,  48, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96,  96, 64, 256, 2,  64,  48,  48,  48, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112,  8, 128, 2, 128,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 16, 128, 2,  64,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 32, 128, 2,  64,  56,  56,  56, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 64, 256, 2,  64,  56,  56,  56, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128,  8, 128, 2, 128,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 16, 128, 2,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 32, 128, 2,  64,  64,  64,  64, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 64, 256, 2,  64,  64,  64,  64, 1, true);
+
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256,  8,  64, 4,  64, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16,  64, 4,  32, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2,  32, 128, 128, 128, 1, true);
+    GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 256, 2,  32, 128, 128, 128, 1, true);
+
+    // Fallback for unsupported DKQ values (e.g. 576). Must return non-zero values to satisfy
+    // compile-time static_asserts even though the kernel guard prevents runtime execution.
+    // nthreads=256 gives nwarps=4 (warp_size=64) or 8 (warp_size=32), nbatch_fa=128 satisfies np*16 divisibility.
+    return fattn_mma_config(256, 1, 128, 4, 4, 4, 1, false);
+}
+
 static __host__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, const int DV, const int ncols, const int cc) {
    if (ampere_mma_available(cc)) {
        return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
@@ -118,6 +156,9 @@ static __host__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, c
    if (turing_mma_available(cc)) {
        return ggml_cuda_fattn_mma_get_config_turing(DKQ, DV, ncols);
    }
+    if (amd_mfma_available(cc)) {
+        return ggml_cuda_fattn_mma_get_config_cdna(DKQ, DV, ncols);
+    }
    if (amd_wmma_available(cc)) {
        return ggml_cuda_fattn_mma_get_config_rdna(DKQ, DV, ncols);
    }
@@ -130,6 +171,8 @@ static constexpr __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config(cons
    return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
 #elif defined(TURING_MMA_AVAILABLE)
    return ggml_cuda_fattn_mma_get_config_turing(DKQ, DV, ncols);
+#elif defined(AMD_MFMA_AVAILABLE)
+    return ggml_cuda_fattn_mma_get_config_cdna(DKQ, DV, ncols);
 #elif defined(VOLTA_MMA_AVAILABLE)
    return ggml_cuda_fattn_mma_get_config_volta(DKQ, DV, ncols);
 #elif defined(AMD_WMMA_AVAILABLE)
@@ -205,15 +248,15 @@ static constexpr __device__ bool ggml_cuda_fattn_mma_get_Q_in_reg(const int DKQ,
 }

 static constexpr __device__ int get_cols_per_thread() {
-#if defined(AMD_WMMA_AVAILABLE)
-    return 1; // RDNA has a single column.
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
+    return 1; // AMD has a single column per thread.
 #else
    return 2; // This is specifically KQ columns, Volta only has a single VKQ column.
-#endif // defined(AMD_WMMA_AVAILABLE)
+#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
 }

 static __host__ int get_cols_per_warp(const int cc) {
-    if (turing_mma_available(cc) || amd_wmma_available(cc)) {
+    if (turing_mma_available(cc) || amd_wmma_available(cc) || amd_mfma_available(cc)) {
        return 16;
    } else {
        // Volta
@@ -241,6 +284,7 @@ static constexpr __device__ int ggml_cuda_fattn_mma_get_nstages(const int DKQ, c
 template<int stride_tile, int nwarps, int nbatch_fa, bool use_cp_async, bool oob_check>
 static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(
        const half2 * const __restrict__ KV, half2 * const __restrict__ tile_KV, const int D2, const int stride_KV, const int i_sup) {
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    // K/V data is loaded with decreasing granularity for D for better memory bandwidth.
    // The minimum granularity with cp.async is 16 bytes, with synchronous data loading it's 4 bytes.
    if constexpr (use_cp_async) {
@@ -252,10 +296,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(
        const unsigned int tile_KV_32 = ggml_cuda_cvta_generic_to_shared(tile_KV);

        auto load = [&] __device__ (auto n) {
-            const int stride_k = WARP_SIZE >> n;
-            const int k0_start = stride_k == WARP_SIZE ? 0 : chunks_per_row - chunks_per_row % (2*stride_k);
+            const int stride_k = warp_size >> n;
+            const int k0_start = stride_k == warp_size ? 0 : chunks_per_row - chunks_per_row % (2*stride_k);
            const int k0_stop  =                             chunks_per_row - chunks_per_row % (1*stride_k);
-            const int stride_i = WARP_SIZE / stride_k;
+            const int stride_i = warp_size / stride_k;

            if (k0_start == k0_stop) {
                return;
@@ -263,7 +307,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(

 #pragma unroll
            for (int i0 = 0; i0 < nbatch_fa; i0 += nwarps*stride_i) {
-                const int i = i0 + threadIdx.y*stride_i + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+                const int i = i0 + threadIdx.y*stride_i + (stride_k == warp_size ? 0 : threadIdx.x / stride_k);

                if (i0 + nwarps*stride_i > nbatch_fa && i >= nbatch_fa) {
                    break;
@@ -271,7 +315,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(

 #pragma unroll
                for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
-                    const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+                    const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k);

                    cp_async_cg_16<preload>(tile_KV_32 + i*(stride_tile*sizeof(half2)) + k*16, KV + i*stride_KV + k*h2_per_chunk);
                }
@@ -287,10 +331,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(
    } else {
        // TODO use ggml_cuda_memcpy_1
        auto load = [&] __device__ (const int n) {
-            const int stride_k = WARP_SIZE >> n;
-            const int k0_start = stride_k == WARP_SIZE ? 0 : D2 - D2 % (2*stride_k);
+            const int stride_k = warp_size >> n;
+            const int k0_start = stride_k == warp_size ? 0 : D2 - D2 % (2*stride_k);
            const int k0_stop  =                             D2 - D2 % (1*stride_k);
-            const int stride_i = WARP_SIZE / stride_k;
+            const int stride_i = warp_size / stride_k;

            if (k0_start == k0_stop) {
                return;
@@ -298,7 +342,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(

 #pragma unroll
            for (int i0 = 0; i0 < nbatch_fa; i0 += nwarps*stride_i) {
-                const int i = i0 + threadIdx.y*stride_i + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+                const int i = i0 + threadIdx.y*stride_i + (stride_k == warp_size ? 0 : threadIdx.x / stride_k);

                if (i0 + nwarps*stride_i > nbatch_fa && i >= nbatch_fa) {
                    break;
@@ -306,7 +350,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(

 #pragma unroll
                for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
-                    const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+                    const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k);

                    tile_KV[i*stride_tile + k] = !oob_check || i < i_sup ? KV[i*stride_KV + k] : make_half2(0.0f, 0.0f);
                }
@@ -324,18 +368,19 @@ template<int ncols1, int nwarps, int nbatch_fa, bool use_cp_async, bool oob_chec
 static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
        const half * const __restrict__ mask_h, half * const __restrict__ tile_mask,
        const int stride_mask, const int i_sup, const int j0, const uint3 ne01) {
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    if constexpr (use_cp_async) {
-        static_assert(nbatch_fa <= 8*WARP_SIZE && nbatch_fa % 8 == 0, "bad nbatch_fa");
+        static_assert(nbatch_fa <= 8*warp_size && nbatch_fa % 8 == 0, "bad nbatch_fa");
        static_assert(!oob_check, "OOB check incompatible with cp_async");
        constexpr int preload = nbatch_fa >= 32 ? nbatch_fa * sizeof(half) : 64;
-        constexpr int cols_per_warp = 8*WARP_SIZE/nbatch_fa;
+        constexpr int cols_per_warp = 8*warp_size/nbatch_fa;
        constexpr int stride_j = nwarps * cols_per_warp;

        const unsigned int tile_mask_32 = ggml_cuda_cvta_generic_to_shared(tile_mask);

 #pragma unroll
        for (int j1 = 0; j1 < ncols1; j1 += stride_j) {
-            const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (WARP_SIZE/cols_per_warp);
+            const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (warp_size/cols_per_warp);
            const int j_vram = fastmodulo(j0 + j_sram, ne01);

            if (j1 + stride_j > ncols1 && j_sram >= ncols1) {
@@ -357,25 +402,25 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
            }

 #pragma unroll
-            for (int i0 = 0; i0 < nbatch_fa; i0 += WARP_SIZE) {
+            for (int i0 = 0; i0 < nbatch_fa; i0 += warp_size) {
                const int i = i0 + threadIdx.x;

                tile_mask[j_sram*(nbatch_fa + 8) + i] = i < i_sup ? mask_h[j_vram*stride_mask + i] : half(0.0f);
            }
        }
-    } else if constexpr (nbatch_fa < 2*WARP_SIZE) {
-        constexpr int cols_per_warp = 2*WARP_SIZE/nbatch_fa;
+    } else if constexpr (nbatch_fa < 2*warp_size) {
+        constexpr int cols_per_warp = 2*warp_size/nbatch_fa;
        constexpr int stride_j = nwarps * cols_per_warp;
 #pragma unroll
        for (int j1 = 0; j1 < ncols1; j1 += stride_j) {
-            const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (WARP_SIZE/cols_per_warp);
+            const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (warp_size/cols_per_warp);
            const int j_vram = fastmodulo(j0 + j_sram, ne01);

            if (j1 + stride_j > ncols1 && j_sram >= ncols1) {
                break;
            }

-            const int i = threadIdx.x % (WARP_SIZE/cols_per_warp);
+            const int i = threadIdx.x % (warp_size/cols_per_warp);

            ggml_cuda_memcpy_1<sizeof(half2)>(tile_mask + j_sram*(nbatch_fa + 8) + 2*i, mask_h + j_vram*stride_mask + 2*i);
        }
@@ -390,7 +435,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
            }

 #pragma unroll
-            for (int i0 = 0; i0 < nbatch_fa; i0 += 2*WARP_SIZE) {
+            for (int i0 = 0; i0 < nbatch_fa; i0 += 2*warp_size) {
                const int i = i0 + 2*threadIdx.x;

                ggml_cuda_memcpy_1<sizeof(half2)>(tile_mask + j_sram*(nbatch_fa + 8) + i, mask_h + j_vram*stride_mask + i);
@@ -428,7 +473,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
        const int jt,
        const int kb0,
        const int k_VKQ_sup) {
-#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
+    constexpr int  warp_size       = ggml_cuda_get_physical_warp_size();
    constexpr int  ncols           = ncols1 * ncols2;
    constexpr int  cols_per_warp   = T_B_KQ::I;
    constexpr int  cols_per_thread = get_cols_per_thread();
@@ -447,7 +493,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
    const int k_VKQ_0 = kb0 * nbatch_fa;
 #if defined(TURING_MMA_AVAILABLE)
    T_C_KQ KQ_C[nbatch_fa/(np*(cols_per_warp == 8 ? T_C_KQ::I : T_C_KQ::J))];
-#elif defined(AMD_WMMA_AVAILABLE)
+#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
    T_C_KQ KQ_C[nbatch_fa/(np*T_C_KQ::J)];
 #else // Volta
    T_C_KQ KQ_C[nbatch_fa/(np*T_C_KQ::J)];
@@ -500,13 +546,13 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                        mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[k_KQ_0/T_A_KQ::J]);
                    } else {
                        // Wide version of KQ_C is column-major
-#if defined(AMD_WMMA_AVAILABLE)
-                        // RDNA matrix C is column-major.
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
+                        // AMD matrix C is column-major.
                        mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[k_KQ_0/T_A_KQ::J]);
 #else
                        // swap A and B for CUDA.
                        mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[k_KQ_0/T_A_KQ::J], K_A);
-#endif // defined(AMD_WMMA_AVAILABLE)
+#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    }
                }
            }
@@ -526,13 +572,13 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                        mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[0]);
                    } else {
                        // Wide version of KQ_C is column-major
-#if defined(AMD_WMMA_AVAILABLE)
-                        // RDNA matrix C is column-major.
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
+                        // AMD matrix C is column-major.
                        mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[0]);
 #else
                        // swap A and B for CUDA.
                        mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[0], K_A);
-#endif // defined(AMD_WMMA_AVAILABLE)
+#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    }
                }
            }
@@ -585,12 +631,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
            for (int l = 0; l < T_C_KQ::ne; ++l) {
                if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::I + T_C_KQ::get_i(l) < k_VKQ_sup) {
-#if defined(AMD_WMMA_AVAILABLE)
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    constexpr int KQ_idx = 0;
 #else
                    // Turing + Volta:
                    const int KQ_idx = l % 2;
-#endif // defined(AMD_WMMA_AVAILABLE)
+#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    KQ_max_new[KQ_idx] = fmaxf(KQ_max_new[KQ_idx], KQ_C[k0/(np*T_C_KQ::I)].x[l] + FATTN_KQ_MAX_OFFSET);
                }
            }
@@ -601,7 +647,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
        for (int col = 0; col < cols_per_thread; ++col) {
 #pragma unroll
            for (int offset = 16; offset >= 4; offset >>= 1) {
-                KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, WARP_SIZE));
+                KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, warp_size));
            }
        }

@@ -611,12 +657,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
            for (int l = 0; l < T_C_KQ::ne; ++l) {
                if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::I + T_C_KQ::get_i(l) < k_VKQ_sup) {
-#if defined(AMD_WMMA_AVAILABLE)
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    constexpr int KQ_idx = 0;
 #else
                    // Turing + Volta:
                    const int KQ_idx = l % 2;
-#endif // defined(AMD_WMMA_AVAILABLE)
+#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    KQ_C[k0/(np*T_C_KQ::I)].x[l] = expf(KQ_C[k0/(np*T_C_KQ::I)].x[l] - KQ_max_new[KQ_idx]);
                    KQ_rowsum_add[KQ_idx] += KQ_C[k0/(np*T_C_KQ::I)].x[l];
                } else {
@@ -649,12 +695,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
            for (int l = 0; l < T_C_KQ::ne; ++l) {
                if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::J + T_C_KQ::get_j(l) < k_VKQ_sup) {
-#if defined(AMD_WMMA_AVAILABLE)
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    constexpr int KQ_idx = 0;
 #else
                    // Turing + Volta:
                    const int KQ_idx = (l/2) % 2;
-#endif // defined(AMD_WMMA_AVAILABLE)
+#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    KQ_max_new[KQ_idx] = fmaxf(KQ_max_new[KQ_idx], KQ_C[(k0/(np*T_C_KQ::J))].x[l] + FATTN_KQ_MAX_OFFSET);
                }
            }
@@ -666,6 +712,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
            // Values per KQ column are spread across 4 threads:
            constexpr int offset_first = 2;
            constexpr int offset_last  = 1;
+#elif defined(AMD_MFMA_AVAILABLE)
+            // MFMA: 4 threads per Q column (threadIdx.x % 16 == col, spaced by 16).
+            constexpr int offset_first = 32;
+            constexpr int offset_last  = 16;
 #elif defined(AMD_WMMA_AVAILABLE)
            // Values per KQ column are spread across 2 threads:
            constexpr int offset_first = 16;
@@ -677,7 +727,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #endif // defined(TURING_MMA_AVAILABLE)
 #pragma unroll
            for (int offset = offset_first; offset >= offset_last; offset >>= 1) {
-                KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, WARP_SIZE));
+                KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, warp_size));
            }
        }

@@ -687,12 +737,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
 #pragma unroll
            for (int l = 0; l < T_C_KQ::ne; ++l) {
                if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::J + T_C_KQ::get_j(l) < k_VKQ_sup) {
-#if defined(AMD_WMMA_AVAILABLE)
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    constexpr int KQ_idx = 0;
 #else
                    // Turing + Volta:
                    const int KQ_idx = (l/2) % 2;
-#endif // defined(AMD_WMMA_AVAILABLE)
+#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                    KQ_C[(k0/(np*T_C_KQ::J))].x[l] = expf(KQ_C[(k0/(np*T_C_KQ::J))].x[l] - KQ_max_new[KQ_idx]);
                    KQ_rowsum_add[KQ_idx] += KQ_C[(k0/(np*T_C_KQ::J))].x[l];
                } else {
@@ -739,7 +789,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                }
            }
        }
-#elif defined(AMD_WMMA_AVAILABLE)
+#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
        const half2 KQ_max_scale_h2 = make_half2(
            KQ_max_scale[0], KQ_max_scale[0]);
 #pragma unroll
@@ -818,7 +868,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
        }
        const half2 * tile_V_i = !V_is_K_view || i0_stop > 2*nbatch_K2 ? tile_V : tile_V + i0_start/2;

-#if defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+#if defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
        constexpr int i0_stride = cols_per_warp == 8 ? T_C_VKQ::I : 2*T_C_VKQ::J;
 #pragma unroll
        for (int i_VKQ_0 = i0_start; i_VKQ_0 < i0_stop; i_VKQ_0 += i0_stride) {
@@ -830,24 +880,38 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                T_A_VKQ A; // Transposed in SRAM but not in registers, gets transposed on load.
 #if defined(LDMATRIX_TRANS_AVAILABLE)
                load_ldmatrix_trans(A, tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V);
+#elif defined(AMD_MFMA_AVAILABLE)
+                // MFMA A register layout: A_mat[i=lane%16][k=4*(lane/16)+reg].
+                // Normal load gives A_mat[seq][dv] but we need A_mat[dv][seq] = V^T.
+                // Load with transposed addressing: 4 strided half loads.
+                {
+                    const half2 * xs0 = tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2;
+                    const half * xs0_h = (const half *) xs0;
+                    const int stride_h = stride_tile_V * 2; // stride in half units
+                    half * A_h = (half *) A.x;
+#pragma unroll
+                    for (int l = 0; l < 4; ++l) {
+                        A_h[l] = xs0_h[(4*(threadIdx.x / 16) + l) * stride_h + threadIdx.x % 16];
+                    }
+                }
 #else
                // TODO: Try to transpose tile_V when loading gmem to smem.
                // Use mma to transpose T_A_VKQ for RDNA.
                T_A_VKQ A_trans;
                load_ldmatrix(A_trans, tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V);
                mma(A, A_trans, A_identity);
-#endif // defined(TURING_MMA_AVAILABLE)
+#endif // defined(LDMATRIX_TRANS_AVAILABLE)
                if constexpr (T_B_KQ::I == 8) {
                    mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]);
                } else {
                    // Wide version of VKQ_C is column-major.
-#if defined(AMD_WMMA_AVAILABLE)
-                    // RDNA matrix C is column-major.
+#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
+                    // AMD matrix C is column-major.
                    mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]);
 #else
                    // swap A and B for CUDA.
                    mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::J)], A);
-#endif // defined(AMD_WMMA_AVAILABLE)
+#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
                }
            }
        }
@@ -866,7 +930,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
                mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::I)], A);
            }
        }
-#endif // defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+#endif // defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)

        if constexpr (nstages <= 1) {
            __syncthreads(); // Only needed if tile_K == tile_V.
@@ -879,7 +943,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
        tile_Q, tile_K, tile_V, tile_mask,
        Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
    NO_DEVICE_CODE;
-#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
 }

 #if defined(TURING_MMA_AVAILABLE)
@@ -899,7 +963,7 @@ template<> struct mma_tile_sizes<8> {
    using T_B_VKQ = tile< 8,  8, half2>; // column-major
    using T_C_VKQ = tile<16,  4, half2>; // row-major
 };
-#elif defined(AMD_WMMA_AVAILABLE)
+#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
 template<int ncols> struct mma_tile_sizes {
    using T_A_KQ  = tile<16,  8, half2>; // row-major
    using T_B_KQ  = tile<16,  8, half2>; // column-major
@@ -944,9 +1008,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        const int zt_gqa,
        const int kb0_start,
        const int kb0_stop) {
-#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.

+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    constexpr int ncols = ncols1 * ncols2;
    using     T_A_KQ    = typename mma_tile_sizes<ncols>::T_A_KQ;
    using     T_B_KQ    = typename mma_tile_sizes<ncols>::T_B_KQ;
@@ -986,7 +1051,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
    T_B_KQ    Q_B[(Q_in_reg ? DKQ/(2*T_B_KQ::J) : 1)];
 #if defined(TURING_MMA_AVAILABLE)
    T_C_VKQ VKQ_C[cols_per_warp == 8 ? DV/T_C_VKQ::I : DV/(2*T_C_VKQ::J)];
-#elif defined(AMD_WMMA_AVAILABLE)
+#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
    T_C_VKQ VKQ_C[                                     DV/(2*T_C_VKQ::J)];
 #else // Volta
    T_C_VKQ VKQ_C[                                     DV/(2*T_C_VKQ::J)];
@@ -1004,10 +1069,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
    // The loading is done with decreasing granularity for D for better memory bandwidth.
    const half2 scale_h2 = make_half2(scale, scale);
 #pragma unroll
-    for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
-        const int k0_start  = stride_k == WARP_SIZE ? 0 : DKQ/2 - (DKQ/2) % (2*stride_k);
+    for (int stride_k : {warp_size, warp_size/2, warp_size/4, warp_size/8}) {
+        const int k0_start  = stride_k == warp_size ? 0 : DKQ/2 - (DKQ/2) % (2*stride_k);
        const int k0_stop   =                             DKQ/2 - (DKQ/2) % (1*stride_k);
-        const int stride_jc = WARP_SIZE / stride_k;
+        const int stride_jc = warp_size / stride_k;

        if (k0_start == k0_stop) {
            continue;
@@ -1015,7 +1080,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(

 #pragma unroll
        for (int jc0 = 0; jc0 < ncols; jc0 += nwarps*stride_jc) {
-            const int jc = jc0 + threadIdx.y*stride_jc + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+            const int jc = jc0 + threadIdx.y*stride_jc + (stride_k == warp_size ? 0 : threadIdx.x / stride_k);

            if (jc0 + nwarps*stride_jc > ncols && jc >= ncols) {
                break;
@@ -1027,7 +1092,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
            if ((ncols1 == 1 || jt*ncols1 + j < int(ne01.z)) && (ncols2 == 1 || zt_gqa*ncols2 + c < gqa_ratio)) {
 #pragma unroll
                for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
-                    const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+                    const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k);

                    const float2 tmp = Q_f2[(jt*ncols1 + j)*stride_Q1 + c*stride_Q2 + k];
                    tile_Q[jc*stride_tile_Q + k] = scale_h2 * make_half2(tmp.x, tmp.y);
@@ -1035,7 +1100,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
            } else {
 #pragma unroll
                for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
-                    const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+                    const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k);

                    tile_Q[jc*stride_tile_Q + k] = make_half2(0.0f, 0.0f);
                }
@@ -1127,6 +1192,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        // The partial sums are spread across 8/4 threads.
        constexpr int offset_first = cols_per_warp == 8 ? 16 : 2;
        constexpr int offset_last  = cols_per_warp == 8 ?  4 : 1;
+#elif defined(AMD_MFMA_AVAILABLE)
+        // The partial sums are spread across 4 threads (wavefront64, 16 cols).
+        constexpr int offset_first = 32;
+        constexpr int offset_last  = 16;
 #elif defined(AMD_WMMA_AVAILABLE)
        // The partial sums are spread across 2 threads.
        constexpr int offset_first = 16;
@@ -1140,7 +1209,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        for (int col = 0; col < cols_per_thread; ++col) {
 #pragma unroll
            for (int offset = offset_first; offset >= offset_last; offset >>= 1) {
-                KQ_rowsum[col] += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum[col], offset, WARP_SIZE);
+                KQ_rowsum[col] += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum[col], offset, warp_size);
            }
        }
    }
@@ -1189,7 +1258,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
                }
            }
        }
-#elif defined(AMD_WMMA_AVAILABLE)
+#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
        const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[0]);
 #pragma unroll
        for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
@@ -1249,7 +1318,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        const int jc_cwm = threadIdx.y*cols_per_warp + T_C_VKQ::get_i(threadIdx.x % 4);
        const float2 KQ_cmr = make_float2(KQ_max[threadIdx.x % cols_per_thread], KQ_rowsum[threadIdx.x % cols_per_thread]);
        const bool thread_should_write = threadIdx.x % 4 < cols_per_thread;
-#elif defined(AMD_WMMA_AVAILABLE)
+#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
        const int jc_cwm = threadIdx.y*cols_per_warp + T_C_VKQ::get_i(0);
        const float2 KQ_cmr = make_float2(KQ_max[0], KQ_rowsum[0]);
        const bool thread_should_write = threadIdx.x / 16 < cols_per_thread;
@@ -1283,14 +1352,14 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        // Warps with threadIdx.y % np != 0 must NOT return early.
        // All threads must return simultaneously to avoid race conditions with work on the next tile.

-        constexpr int nmeta = np*cols_per_warp >= WARP_SIZE ? np*cols_per_warp/WARP_SIZE : 1;
+        constexpr int nmeta = np*cols_per_warp >= warp_size ? np*cols_per_warp/warp_size : 1;

-        const int jc_meta = threadIdx.y*cols_per_warp + (np*cols_per_warp < WARP_SIZE ? threadIdx.x % (np*cols_per_warp) : threadIdx.x);
+        const int jc_meta = threadIdx.y*cols_per_warp + (np*cols_per_warp < warp_size ? threadIdx.x % (np*cols_per_warp) : threadIdx.x);
        float2 * const meta_ptr = ((float2 *) tile_Q) + jc_meta*(tile_stride/2) + nbatch_combine/2;
        float2 meta[nmeta];
 #pragma unroll
        for (int imeta = 0; imeta < nmeta; ++imeta) {
-            meta[imeta] = meta_ptr[imeta * WARP_SIZE * tile_stride/2];
+            meta[imeta] = meta_ptr[imeta * warp_size * tile_stride/2];
        }

        float KQ_cmn = meta[0].x; // KQ combine max new, max between all parallel warps.
@@ -1300,8 +1369,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        }
 #pragma unroll
        for (int offset = np*cols_per_warp/2; offset >= cols_per_warp; offset >>= 1) {
-            if (offset < WARP_SIZE) {
-                KQ_cmn = fmaxf(KQ_cmn, __shfl_xor_sync(0xFFFFFFFF, KQ_cmn, offset, WARP_SIZE));
+            if (offset < warp_size) {
+                KQ_cmn = fmaxf(KQ_cmn, __shfl_xor_sync(0xFFFFFFFF, KQ_cmn, offset, warp_size));
            }
        }

@@ -1318,8 +1387,8 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        }
 #pragma unroll
        for (int offset = np*cols_per_warp/2; offset >= cols_per_warp; offset >>= 1) {
-            if (offset < WARP_SIZE) {
-                KQ_crs += __shfl_xor_sync(0xFFFFFFFF, KQ_crs, offset, WARP_SIZE);
+            if (offset < warp_size) {
+                KQ_crs += __shfl_xor_sync(0xFFFFFFFF, KQ_crs, offset, warp_size);
            }
        }

@@ -1328,19 +1397,19 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        // Write back combined meta data:
 #pragma unroll
        for (int imeta = 0; imeta < nmeta; ++imeta) {
-            if (np*cols_per_warp >= WARP_SIZE || threadIdx.x < np*cols_per_warp) {
+            if (np*cols_per_warp >= warp_size || threadIdx.x < np*cols_per_warp) {
                // Combined KQ max scale + rowsum.
-                meta_ptr[imeta * WARP_SIZE * tile_stride/2] = make_float2(KQ_cms[imeta], KQ_crs);
+                meta_ptr[imeta * warp_size * tile_stride/2] = make_float2(KQ_cms[imeta], KQ_crs);
            }
        }

        // Combined KQ max + rowsum.
-        static_assert(cols_per_warp <= WARP_SIZE);
-        if (needs_fixup && (cols_per_warp == WARP_SIZE || threadIdx.x < cols_per_warp)) {
+        static_assert(cols_per_warp <= warp_size);
+        if (needs_fixup && (cols_per_warp == warp_size || threadIdx.x < cols_per_warp)) {
            float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
            dstk_fixup_meta[(threadIdx.y/np)*cols_per_warp + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
        }
-        if (is_fixup && (cols_per_warp == WARP_SIZE || threadIdx.x < cols_per_warp)) {
+        if (is_fixup && (cols_per_warp == warp_size || threadIdx.x < cols_per_warp)) {
            float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
            dstk_fixup_meta[(threadIdx.y/np)*cols_per_warp + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
        }
@@ -1388,10 +1457,10 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
            float2 * dstk_fixup_data = dstk_fixup + gridDim.x*(2*ncols) + blockIdx.x*(ncols*(DV/2));

 #pragma unroll
-            for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
-                const int k0_start  = stride_k == WARP_SIZE ? 0 : nbatch_combine - nbatch_combine % (2*stride_k);
+            for (int stride_k : {warp_size, warp_size/2, warp_size/4, warp_size/8}) {
+                const int k0_start  = stride_k == warp_size ? 0 : nbatch_combine - nbatch_combine % (2*stride_k);
                const int k0_stop   =                             nbatch_combine - nbatch_combine % (1*stride_k);
-                const int stride_jc = WARP_SIZE / stride_k;
+                const int stride_jc = warp_size / stride_k;

                if (k0_start == k0_stop) {
                    continue;
@@ -1399,7 +1468,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(

 #pragma unroll
                for (int jc0_dst = 0; jc0_dst < ncols; jc0_dst += (nwarps/np)*stride_jc) {
-                    const int jc_dst = jc0_dst + (threadIdx.y/np)*stride_jc + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+                    const int jc_dst = jc0_dst + (threadIdx.y/np)*stride_jc + (stride_k == warp_size ? 0 : threadIdx.x / stride_k);

                    if (jc0_dst + (nwarps/np)*stride_jc > ncols && jc_dst >= ncols) {
                        break;
@@ -1417,7 +1486,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
                    const float * meta_j = (const float *) tile_Q + jc_tile_K*tile_stride + nbatch_combine;
 #pragma unroll
                    for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
-                        const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+                        const int k = k0 + (stride_k == warp_size ? threadIdx.x : threadIdx.x % stride_k);

                        float2 dstk_val = make_float2(0.0f, 0.0f);
 #pragma unroll
@@ -1453,7 +1522,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
        stride_Q1, stride_Q2, stride_K, stride_V, stride_mask,
        jt, kb0_start, kb0_stop);
    NO_DEVICE_CODE;
-#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4))
+#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
 }

 template<int DKQ, int DV, int ncols1, int ncols2, bool use_logit_softcap, bool V_is_K_view>
@@ -1480,7 +1549,7 @@ static __global__ void flash_attn_ext_f16(
                            const int32_t nb21, const int32_t nb22, const int64_t nb23,
                            const int32_t ne31, const int32_t ne32, const int32_t ne33,
                            const int32_t nb31, const int32_t nb32, const int64_t nb33) {
-#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)))
+#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE))

    // Skip unused kernel variants for faster compilation:
    if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) {
@@ -1508,10 +1577,18 @@ static __global__ void flash_attn_ext_f16(
    }
 #endif // defined(AMD_WMMA_AVAILABLE)

+#if defined(AMD_MFMA_AVAILABLE)
+    if (DKQ != 64 && DKQ != 80 && DKQ != 96 && DKQ != 112 && DKQ != 128) {
+        NO_DEVICE_CODE;
+        return;
+    }
+#endif // defined(AMD_MFMA_AVAILABLE)
+
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    constexpr int ncols     = ncols1 * ncols2;
    constexpr int nbatch_fa = ggml_cuda_fattn_mma_get_nbatch_fa(DKQ, DV, ncols);
    constexpr int nthreads  = ggml_cuda_fattn_mma_get_nthreads(DKQ, DV, ncols);
-    constexpr int nwarps    = nthreads / WARP_SIZE;
+    constexpr int nwarps    = nthreads / warp_size;

    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.

@@ -1624,7 +1701,7 @@ static __global__ void flash_attn_ext_f16(
              ne31, ne32, ne33,
              nb31, nb32, nb33);
    NO_DEVICE_CODE;
-#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)))
+#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE))
 }

 template <int DKQ, int DV, int ncols1, int ncols2>
@@ -1644,7 +1721,8 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
    const int  nstages        = ggml_cuda_fattn_mma_get_nstages       (DKQ, DV, ncols1, ncols2, cc);

    const int cols_per_warp = std::min(ncols, get_cols_per_warp(cc));
-    const int nwarps        = nthreads / WARP_SIZE;
+    const int warp_size_host = ggml_cuda_info().devices[ctx.device].warp_size;
+    const int nwarps         = nthreads / warp_size_host;

    constexpr bool V_is_K_view = DKQ == 576; // Guaranteed by the kernel selection logic in fattn.cu

@@ -1694,7 +1772,7 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
    }

    launch_fattn<DV, ncols1, ncols2>
-        (ctx, dst, fattn_kernel, nwarps, nbytes_shared_total, nbatch_fa, true, true, true);
+        (ctx, dst, fattn_kernel, nwarps, nbytes_shared_total, nbatch_fa, true, true, true, warp_size_host);
 }


@@ -440,6 +440,18 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
        return BEST_FATTN_KERNEL_MMA_F16;
    }

+    // Use MFMA flash attention for CDNA (MI100+):
+    if (amd_mfma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 256 && Q->ne[0] != 576) {
+        const int64_t eff_nq = Q->ne[1] * (gqa_opt_applies ? gqa_ratio : 1);
+        // MMA vs tile crossover benchmarked on MI300X @ d32768:
+        //   hsk=64  (gqa=4): MMA wins at eff >= 128 (+11%)
+        //   hsk=128 (gqa=4): MMA wins at eff >= 128 (+4%)
+        if (eff_nq >= (GGML_CUDA_CC_IS_CDNA1(cc) && Q->ne[0] == 64 ? 64 : 128)) {
+            return BEST_FATTN_KERNEL_MMA_F16;
+        }
+        // Fall through to tile kernel for small effective batch sizes.
+    }
+
    // If there are no tensor cores available, use the generic tile kernel:
    if (can_use_vector_kernel) {
        if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) {
@@ -668,7 +668,7 @@ namespace ggml_cuda_mma {

        return ret;
    }
-#elif defined(AMD_WMMA_AVAILABLE)
+#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
    template <int I, int J>
    static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {
        tile<I, J/2, half2> ret;
@@ -964,6 +964,34 @@ namespace ggml_cuda_mma {
        GGML_UNUSED_VARS(D, A, B);
        NO_DEVICE_CODE;
 #endif // defined(RDNA4)
+#elif defined(AMD_MFMA_AVAILABLE)
+        // MFMA: FP16 input, FP32 accumulate, convert back to half2.
+        using halfx4_t = __attribute__((ext_vector_type(4))) _Float16;
+        using floatx4_t = __attribute__((ext_vector_type(4))) float;
+
+        // Convert existing half2 accumulator to float for MFMA:
+        floatx4_t acc_f32;
+        {
+            const halfx4_t acc_h = reinterpret_cast<const halfx4_t&>(D.x[0]);
+#pragma unroll
+            for (int i = 0; i < 4; ++i) {
+                acc_f32[i] = (float)acc_h[i];
+            }
+        }
+
+        const halfx4_t& a_frag = reinterpret_cast<const halfx4_t&>(A.x[0]);
+        const halfx4_t& b_frag = reinterpret_cast<const halfx4_t&>(B.x[0]);
+        acc_f32 = __builtin_amdgcn_mfma_f32_16x16x16f16(a_frag, b_frag, acc_f32, 0, 0, 0);
+
+        // Convert back to half2:
+        {
+            halfx4_t result_h;
+#pragma unroll
+            for (int i = 0; i < 4; ++i) {
+                result_h[i] = (_Float16)acc_f32[i];
+            }
+            reinterpret_cast<halfx4_t&>(D.x[0]) = result_h;
+        }
 #else
        GGML_UNUSED_VARS(D, A, B);
        NO_DEVICE_CODE;
@@ -55,7 +55,11 @@ void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
  const int32_t* src2_d = (const int32_t*)src2->data;
  float* dst_d = (float*)dst->data;

-  int threads = std::min((int)ne00, 768);  // cols
+  const unsigned int max_work_group_size = ggml_sycl_info().max_work_group_sizes[ctx.device];
+  assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
+
+  int threads = std::min((unsigned int)ne00, max_work_group_size);  // cols
+
  ctx.stream()->parallel_for(
      sycl::nd_range<3>(
          sycl::range<3>(1, ne02, ne01) * sycl::range<3>(1, 1, threads),
@@ -11,8 +11,8 @@ static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
        int ne0, int ne1, int ne2, int ne3,
        int ne10, int ne11, int ne12, int ne13,
        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s00,*/ int s01, int s02, int s03,
-        /*int s10,*/ int s11, int s12, int s13,
+        int s00, int s01, int s02, int s03,
+        int s10, int s11, int s12, int s13,
        const sycl::nd_item<3> &item_ct1) {
    const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
                    item_ct1.get_local_id(2);
@@ -44,7 +44,7 @@ static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
    for (int i0 = i0s; i0 < ne0;
         i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) {
        const int i10 = i0 % ne10;
-        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0*s00] : 0.0f, (float)src1_row[i10*s10]);
    }
 }

@@ -53,8 +53,8 @@ static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t
        int ne0, int ne1, int ne2, int ne3,
        int ne10, int ne11, int ne12, int ne13,
        /*int s0, */ int s1,  int s2,  int s3,
-        /*int s00,*/ int s01, int s02, int s03,
-        /*int s10,*/ int s11, int s12, int s13,
+        int s00, int s01, int s02, int s03,
+        int s10, int s11, int s12, int s13,
        const sycl::nd_item<3> &item_ct1) {

    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
@@ -82,7 +82,7 @@ static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t
    dst_t * dst_row = dst + i_dst;

    const int i10 = i0 % ne10;
-    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
+    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0*s00] : 0.0f, (float)src1_row[i10*s10]);
 }


@@ -95,7 +95,8 @@ struct bin_bcast_sycl {
                    const int64_t ne3, const size_t nb00, const size_t nb01, const size_t nb02, const size_t nb03,
                    const size_t nb10, const size_t nb11, const size_t nb12, const size_t nb13, const size_t nb0,
                    const size_t nb1, const size_t nb2, const size_t nb3, const bool src0_is_contiguous,
-                    const bool src1_is_contiguous, const bool dst_is_contiguous, queue_ptr stream) {
+                    const bool src1_is_contiguous, const bool src0_is_permuted, const bool src1_is_permuted,
+                    queue_ptr stream) {
        int nr0 = ne10 / ne0;
        int nr1 = ne11/ne1;
        int nr2 = ne12/ne2;
@@ -123,7 +124,7 @@ struct bin_bcast_sycl {
            cnb[3] *= cne[3];
        };

-        if (src0_is_contiguous && src1_is_contiguous && dst_is_contiguous) {
+        if (src0_is_contiguous && src1_is_contiguous && !src0_is_permuted && !src1_is_permuted) {
            for (int i = 0; i < 4; i++) {
                if (nr[i] != 1) {
                    break;
@@ -164,7 +165,7 @@ struct bin_bcast_sycl {
            size_t nb12 = cnb1[2];
            size_t nb13 = cnb1[3];

-            size_t s0 = nb0 / sizeof(dst_t);
+            // size_t s0 = nb0 / sizeof(dst_t);
            size_t s1 = nb1 / sizeof(dst_t);
            size_t s2 = nb2 / sizeof(dst_t);
            size_t s3 = nb3 / sizeof(dst_t);
@@ -196,9 +197,6 @@ struct bin_bcast_sycl {
            GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
            GGML_ASSERT(nb13 % sizeof(src1_t) == 0);

-            GGML_ASSERT(s0 == 1);
-            GGML_ASSERT(s10 == 1);
-
            const int block_size = 128;

            int64_t hne0 = std::max(ne0/2LL, 1LL);
@@ -232,8 +230,8 @@ struct bin_bcast_sycl {
                        [=](sycl::nd_item<3> item_ct1) {
                            k_bin_bcast_unravel<bin_op>(
                                src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3,
-                                ne10, ne11, ne12, ne13, s1, s2, s3, s01, s02,
-                                s03, s11, s12, s13, item_ct1);
+                                ne10, ne11, ne12, ne13, s1, s2, s3, s00, s01, s02,
+                                s03, s10, s11, s12, s13, item_ct1);
                        });
                }
            } else {
@@ -251,7 +249,7 @@ struct bin_bcast_sycl {
                    [=](sycl::nd_item<3> item_ct1) {
                        k_bin_bcast<bin_op>(src0_dd, src1_dd, dst_dd, ne0, ne1,
                                            ne2, ne3, ne10, ne11, ne12, ne13,
-                                            s1, s2, s3, s01, s02, s03, s11, s12, s13,
+                                            s1, s2, s3, s00, s01, s02, s03, s10, s11, s12, s13,
                                            item_ct1);
                    });
            }
@@ -268,24 +266,27 @@ inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_t
    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
        op()((const float *) src0->data, (const float *) src1->data, (float *) dst->data, ne00, ne01, ne02, ne03, ne10,
             ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, nb2, nb3,
-             ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), main_stream);
+             ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1), main_stream);
    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
        op()((const sycl::half *) src0->data, (const sycl::half *) src1->data, (sycl::half *) dst->data, ne00, ne01,
             ne02, ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13,
-             nb0, nb1, nb2, nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst),
+             nb0, nb1, nb2, nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1),
             main_stream);
    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
        op()((const sycl::half *) src0->data, (const float *) src1->data, (sycl::half *) dst->data, ne00, ne01, ne02,
             ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1,
-             nb2, nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), main_stream);
+             nb2, nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1),
+             main_stream);
    } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) {
        op()((const int32_t *) src0->data, (const int32_t *) src1->data, (int32_t *) dst->data, ne00, ne01, ne02, ne03,
             ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, nb2,
-             nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), main_stream);
+             nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1),
+             main_stream);
    } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) {
        op()((const int16_t *) src0->data, (const int16_t *) src1->data, (int16_t *) dst->data, ne00, ne01, ne02, ne03,
             ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, nb2,
-             nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_contiguous(dst), main_stream);
+             nb3, ggml_is_contiguous(src0), ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1),
+             main_stream);
    } else {
        fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, ggml_type_name(dst->type),
                ggml_type_name(src0->type), ggml_type_name(src1->type));
@@ -7,9 +7,21 @@

 #include <cstdint>

+static uint32_t validate_graph_operation(size_t cgraph_size, uint32_t shmem_res_id, const char * operation) {
+    if (cgraph_size == 0) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Zero-size computation graph\n", operation);
+        return 1;
+    }
+
+    // place-holder: validate that the size of shmem_res_id is <= cgraph_size
+    // need to add another method in the Virgl->APIR callback interface
+    GGML_UNUSED(shmem_res_id);
+
+    return 0;  // Valid
+}
+
 uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
    GGML_UNUSED(ctx);
-    GGML_UNUSED(enc);

    static bool async_backend_initialized = false;
    static bool async_backend;
@@ -34,10 +46,26 @@ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, v
    size_t cgraph_size;
    apir_decode_size_t(dec, &cgraph_size);

+    if (validate_graph_operation(cgraph_size, shmem_res_id, __func__) != 0) {
+        apir_decoder_set_fatal(dec);
+        return 1;
+    }
+
    apir_decoder secondary_dec = apir_new_decoder((const char *) shmem_data, cgraph_size);

    ggml_cgraph * cgraph = apir_decode_ggml_cgraph(&secondary_dec, cgraph_size);

+    if (!cgraph || apir_decoder_get_fatal(&secondary_dec)) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Failed to deserialize computation graph\n", __func__);
+        return 1;
+    }
+
+    if (cgraph->n_nodes < 0 || cgraph->n_leafs < 0) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid negative node/leaf count: nodes=%d leafs=%d\n", __func__,
+                       cgraph->n_nodes, cgraph->n_leafs);
+        return 1;
+    }
+
    ggml_status status;
 #if APIR_BACKEND_CHECK_SUPPORTS_OP == 1
    for (int idx = 0; idx < cgraph->n_nodes; idx++) {
@@ -45,7 +73,8 @@ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, v
        if (dev->iface.supports_op(dev, op)) {
            continue;
        }
-        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Graph node %d (%s) not supported by the backend\n", idx, ggml_op_desc(op));
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Graph node %d (%s) not supported by the backend\n", __func__, idx,
+                       ggml_op_desc(op));

        status = GGML_STATUS_ABORTED;
        apir_encode_ggml_status(enc, &status);
@@ -53,9 +82,17 @@ uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, v
        return 0;
    }
 #endif
+
+    // Check if backend is properly initialized
+    if (!bck) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Backend not initialized (bck is null)\n", __func__);
+
+        return 1;
+    }
+
    status = bck->iface.graph_compute(bck, cgraph);

-    if (async_backend) {
+    if (async_backend && bck->iface.synchronize) {
        bck->iface.synchronize(bck);
    }

@@ -85,7 +85,19 @@ uint32_t backend_buffer_type_get_alloc_size(apir_encoder * enc, apir_decoder * d

    const ggml_tensor * op = apir_decode_ggml_tensor_inplace(dec);

-    size_t value = buft->iface.get_alloc_size(buft, op);
+    // Check for decode error
+    if (op == nullptr) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Failed to decode tensor\n", __func__);
+        apir_decoder_set_fatal(dec);
+        return 1;
+    }
+
+    size_t value;
+    if (buft->iface.get_alloc_size) {
+        value = buft->iface.get_alloc_size(buft, op);
+    } else {
+        value = ggml_nbytes(op);  // Default fallback
+    }

    apir_encode_size_t(enc, &value);

@@ -6,11 +6,26 @@

 #include <cstdint>

+static uint32_t validate_buffer_operation(size_t offset, size_t size, const char * operation) {
+    // Only check for critical integer overflow - no arbitrary size limits
+    if (offset > SIZE_MAX - size) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Integer overflow in offset+size: %zu + %zu\n", operation, offset, size);
+        return 1;
+    }
+
+    return 0;  // Valid
+}
+
 uint32_t backend_buffer_get_base(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
    GGML_UNUSED(ctx);
    ggml_backend_buffer_t buffer;
    buffer = apir_decode_ggml_buffer(dec);

+    if (!buffer || apir_decoder_get_fatal(dec)) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__);
+        return 1;
+    }
+
    uintptr_t base = (uintptr_t) buffer->iface.get_base(buffer);
    apir_encode_uintptr_t(enc, &base);

@@ -24,6 +39,11 @@ uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl
    ggml_backend_buffer_t buffer;
    buffer = apir_decode_ggml_buffer(dec);

+    if (!buffer || apir_decoder_get_fatal(dec)) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__);
+        return 1;
+    }
+
    ggml_tensor * tensor;
    // safe to remove the const qualifier here
    tensor = (ggml_tensor *) (uintptr_t) apir_decode_ggml_tensor(dec);
@@ -37,6 +57,10 @@ uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl
    size_t size;
    apir_decode_size_t(dec, &size);

+    if (validate_buffer_operation(offset, size, __func__) != 0) {
+        return 1;
+    }
+
    void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id);

    if (!shmem_data) {
@@ -56,6 +80,11 @@ uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl
    ggml_backend_buffer_t buffer;
    buffer = apir_decode_ggml_buffer(dec);

+    if (!buffer || apir_decoder_get_fatal(dec)) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__);
+        return 1;
+    }
+
    const ggml_tensor * tensor;
    // safe to remove the const qualifier here
    tensor = apir_decode_ggml_tensor(dec);
@@ -69,6 +98,10 @@ uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl
    size_t size;
    apir_decode_size_t(dec, &size);

+    if (validate_buffer_operation(offset, size, __func__) != 0) {
+        return 1;
+    }
+
    void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id);
    if (!shmem_data) {
        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__);
@@ -86,6 +119,11 @@ uint32_t backend_buffer_cpy_tensor(apir_encoder * enc, apir_decoder * dec, virgl
    ggml_backend_buffer_t buffer;
    buffer = apir_decode_ggml_buffer(dec);

+    if (!buffer || apir_decoder_get_fatal(dec)) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__);
+        return 1;
+    }
+
    const ggml_tensor * src;
    // safe to remove the const qualifier here
    src               = apir_decode_ggml_tensor(dec);
@@ -105,6 +143,11 @@ uint32_t backend_buffer_clear(apir_encoder * enc, apir_decoder * dec, virgl_apir
    ggml_backend_buffer_t buffer;
    buffer = apir_decode_ggml_buffer(dec);

+    if (!buffer || apir_decoder_get_fatal(dec)) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__);
+        return 1;
+    }
+
    uint8_t value;
    apir_decode_uint8_t(dec, &value);

@@ -120,6 +163,11 @@ uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virg
    ggml_backend_buffer_t buffer;
    buffer = apir_decode_ggml_buffer(dec);

+    if (!buffer || apir_decoder_get_fatal(dec)) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__);
+        return 1;
+    }
+
    if (!apir_untrack_backend_buffer(buffer)) {
        GGML_LOG_WARN(GGML_VIRTGPU_BCK "%s: unknown buffer %p\n", __func__, (void *) buffer);
        return 1;
@@ -1,6 +1,6 @@
 #include "backend-dispatched.h"
-#include "backend-virgl-apir.h"

+#include "backend-virgl-apir.h"
 #include "ggml-backend-impl.h"
 #include "ggml-backend.h"
 #include "ggml-impl.h"
@@ -28,19 +28,24 @@ uint32_t backend_dispatch_initialize(void * ggml_backend_reg_fct_p) {
        return APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED;
    }

-    if (!reg->iface.get_device_count(reg)) {
-        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed: no device found\n", __func__);
+    size_t device_count = reg->iface.get_device_count(reg);
+    if (!device_count) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: no device found\n", __func__);
        return APIR_BACKEND_INITIALIZE_NO_DEVICE;
    }

    dev = reg->iface.get_device(reg, 0);

    if (!dev) {
-        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed: no device received\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: failed to get device\n", __func__);
        return APIR_BACKEND_INITIALIZE_NO_DEVICE;
    }

    bck = dev->iface.init_backend(dev, NULL);
+    if (!bck) {
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: backend initialization failed\n", __func__);
+        return APIR_BACKEND_INITIALIZE_BACKEND_INIT_FAILED;
+    }

    return APIR_BACKEND_INITIALIZE_SUCCESS;
 }
@@ -32,64 +32,6 @@ uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virg
 /* backend */
 uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx);

-static inline const char * backend_dispatch_command_name(ApirBackendCommandType type) {
-    switch (type) {
-        /* device */
-        case APIR_COMMAND_TYPE_DEVICE_GET_DEVICE_COUNT:
-            return "backend_device_get_device_count";
-        case APIR_COMMAND_TYPE_DEVICE_GET_COUNT:
-            return "backend_device_get_count";
-        case APIR_COMMAND_TYPE_DEVICE_GET_NAME:
-            return "backend_device_get_name";
-        case APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION:
-            return "backend_device_get_description";
-        case APIR_COMMAND_TYPE_DEVICE_GET_TYPE:
-            return "backend_device_get_type";
-        case APIR_COMMAND_TYPE_DEVICE_GET_MEMORY:
-            return "backend_device_get_memory";
-        case APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP:
-            return "backend_device_supports_op";
-        case APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE:
-            return "backend_device_get_buffer_type";
-        case APIR_COMMAND_TYPE_DEVICE_GET_PROPS:
-            return "backend_device_get_props";
-        case APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR:
-            return "backend_device_buffer_from_ptr";
-        /* buffer-type */
-        case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME:
-            return "backend_buffer_type_get_name";
-        case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT:
-            return "backend_buffer_type_get_alignment";
-        case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE:
-            return "backend_buffer_type_get_max_size";
-        case APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST:
-            return "backend_buffer_type_is_host (DEPRECATED)";
-        case APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER:
-            return "backend_buffer_type_alloc_buffer";
-        case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE:
-            return "backend_buffer_type_get_alloc_size";
-        /* buffer */
-        case APIR_COMMAND_TYPE_BUFFER_GET_BASE:
-            return "backend_buffer_get_base";
-        case APIR_COMMAND_TYPE_BUFFER_SET_TENSOR:
-            return "backend_buffer_set_tensor";
-        case APIR_COMMAND_TYPE_BUFFER_GET_TENSOR:
-            return "backend_buffer_get_tensor";
-        case APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR:
-            return "backend_buffer_cpy_tensor";
-        case APIR_COMMAND_TYPE_BUFFER_CLEAR:
-            return "backend_buffer_clear";
-        case APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER:
-            return "backend_buffer_free_buffer";
-        /* backend */
-        case APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE:
-            return "backend_backend_graph_compute";
-
-        default:
-            return "unknown";
-    }
-}
-
 extern "C" {
 static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATCH_TABLE_COUNT] = {

@@ -1,5 +1,6 @@
 #pragma once

+// clang-format off
 #include <cstdint>
 #include <cstddef>

@@ -10,6 +11,7 @@
 #include "shared/apir_backend.h"
 #include "shared/apir_cs.h"
 #include "shared/apir_cs_ggml.h"
+// clang-format on

 #define GGML_VIRTGPU_BCK "ggml-virtgpu-backend: "

@@ -19,7 +19,7 @@ struct virgl_apir_callbacks {
 };

 extern "C" {
-ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks *virgl_cbs);
+ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks * virgl_cbs);
 void                      apir_backend_deinit(uint32_t virgl_ctx_id);
 uint32_t                  apir_backend_dispatcher(uint32_t               virgl_ctx_id,
                                                  virgl_apir_callbacks * virgl_cbs,
@@ -1,6 +1,5 @@
 #include "backend-dispatched.h"
 #include "backend-virgl-apir.h"
-
 #include "shared/api_remoting.h"
 #include "shared/apir_backend.h"
 #include "shared/apir_cs.h"
@@ -17,10 +16,10 @@
 #define GGML_DEFAULT_BACKEND_REG "ggml_backend_init"

 static void * backend_library_handle = NULL;
-static FILE * apir_logfile = NULL;
+static FILE * apir_logfile           = NULL;

 static void log_to_file_callback(enum ggml_log_level level, const char * text, void * user_data) {
-    FILE * logfile = (FILE *)user_data;
+    FILE * logfile = (FILE *) user_data;
    fprintf(logfile, "[%d] %s", level, text);
    fflush(logfile);
 }
@@ -48,9 +47,9 @@ void apir_backend_deinit(uint32_t virgl_ctx_id) {
 }

 #define APIR_GGML_LIBRARY_PATH_KEY "ggml.library.path"
-#define APIR_GGML_LIBRARY_REG_KEY "ggml.library.reg"
+#define APIR_GGML_LIBRARY_REG_KEY  "ggml.library.reg"

-ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks *virgl_cbs) {
+ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct virgl_apir_callbacks * virgl_cbs) {
    const char * dlsym_error;

    const char * apir_log_to_file = getenv(APIR_LLAMA_CPP_LOG_TO_FILE_ENV);
@@ -63,15 +62,13 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct
        }
    }

-    const char * library_name = virgl_cbs->get_config(virgl_ctx_id, APIR_GGML_LIBRARY_PATH_KEY);
+    const char * library_name      = virgl_cbs->get_config(virgl_ctx_id, APIR_GGML_LIBRARY_PATH_KEY);
    const char * virgl_library_reg = virgl_cbs->get_config(virgl_ctx_id, APIR_GGML_LIBRARY_REG_KEY);
-    const char * library_reg = virgl_library_reg ? virgl_library_reg : GGML_DEFAULT_BACKEND_REG;
+    const char * library_reg       = virgl_library_reg ? virgl_library_reg : GGML_DEFAULT_BACKEND_REG;

    if (!library_name) {
-        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
-                       "%s: cannot open the GGML library: env var '%s' not defined\n",
-                       __func__, APIR_LLAMA_CPP_GGML_LIBRARY_PATH_ENV);
-
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: cannot open the GGML library: env var '%s' not defined\n", __func__,
+                       APIR_LLAMA_CPP_GGML_LIBRARY_PATH_ENV);

        return APIR_LOAD_LIBRARY_ENV_VAR_MISSING;
    }
@@ -79,16 +76,14 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct
    backend_library_handle = dlopen(library_name, RTLD_LAZY);

    if (!backend_library_handle) {
-        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
-                       "%s: cannot open the GGML library: %s\n", __func__, dlerror());
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: cannot open the GGML library: %s\n", __func__, dlerror());

        return APIR_LOAD_LIBRARY_CANNOT_OPEN;
    }

    if (!library_reg) {
-        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
-                       "%s: cannot register the GGML library: env var '%s' not defined\n",
-                       __func__, APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: cannot register the GGML library: env var '%s' not defined\n", __func__,
+                       APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV);

        return APIR_LOAD_LIBRARY_ENV_VAR_MISSING;
    }
@@ -96,11 +91,9 @@ ApirLoadLibraryReturnCode apir_backend_initialize(uint32_t virgl_ctx_id, struct
    void * ggml_backend_reg_fct = dlsym(backend_library_handle, library_reg);
    dlsym_error                 = dlerror();
    if (dlsym_error) {
-        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
-                       "%s: cannot find the GGML backend registration symbol '%s' (from %s): %s\n",
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: cannot find the GGML backend registration symbol '%s' (from %s): %s\n",
                       __func__, library_reg, APIR_LLAMA_CPP_GGML_LIBRARY_REG_ENV, dlsym_error);

-
        return APIR_LOAD_LIBRARY_SYMBOL_MISSING;
    }

@@ -132,13 +125,12 @@ uint32_t apir_backend_dispatcher(uint32_t               virgl_ctx_id,

    virgl_apir_context ctx = {
        .ctx_id = virgl_ctx_id,
-        .iface = virgl_cbs,
+        .iface  = virgl_cbs,
    };

    if (cmd_type >= APIR_BACKEND_DISPATCH_TABLE_COUNT) {
-        GGML_LOG_ERROR(GGML_VIRTGPU_BCK
-                       "%s: Received an invalid dispatch index (%d >= %d)\n",
-                        __func__, cmd_type, APIR_BACKEND_DISPATCH_TABLE_COUNT);
+        GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Received an invalid dispatch index (%d >= %d)\n", __func__, cmd_type,
+                       APIR_BACKEND_DISPATCH_TABLE_COUNT);
        return APIR_BACKEND_FORWARD_INDEX_INVALID;
    }

@@ -16,28 +16,32 @@ enum ApirCommandType {
    APIR_COMMAND_TYPE_LOADLIBRARY = 1,
    APIR_COMMAND_TYPE_FORWARD     = 2,

-    APIR_COMMAND_TYPE_LENGTH      = 3,
+    APIR_COMMAND_TYPE_LENGTH = 3,
 };

 typedef uint64_t ApirCommandFlags;

 enum ApirLoadLibraryReturnCode {
    APIR_LOAD_LIBRARY_SUCCESS                        = 0,
+    // these error codes are returned by the Virglrenderer APIR component
    APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR = 1,
    APIR_LOAD_LIBRARY_ALREADY_LOADED                 = 2,
    APIR_LOAD_LIBRARY_ENV_VAR_MISSING                = 3,
    APIR_LOAD_LIBRARY_CANNOT_OPEN                    = 4,
    APIR_LOAD_LIBRARY_SYMBOL_MISSING                 = 5,
-    APIR_LOAD_LIBRARY_INIT_BASE_INDEX                = 6,  // anything above this is a APIR backend library initialization return code
+    // any value greater than this is an APIR *backend library* initialization return code
+    APIR_LOAD_LIBRARY_INIT_BASE_INDEX                = 6,
 };

 enum ApirForwardReturnCode {
-    APIR_FORWARD_SUCCESS         = 0,
-    APIR_FORWARD_NO_DISPATCH_FCT = 1,
-    APIR_FORWARD_TIMEOUT         = 2,
-
-    APIR_FORWARD_BASE_INDEX      = 3,  // anything above this is a APIR backend library forward return code
-} ;
+    APIR_FORWARD_SUCCESS                = 0,
+    // these error codes are returned by the Virglrenderer APIR component
+    APIR_FORWARD_NO_DISPATCH_FCT        = 1,
+    APIR_FORWARD_TIMEOUT                = 2,
+    APIR_FORWARD_FAILED_TO_SYNC_STREAMS = 3,
+    // any value greater than this index an APIR *backend library* forward return code
+    APIR_FORWARD_BASE_INDEX             = 4,
+};

 __attribute__((unused)) static inline const char * apir_command_name(ApirCommandType type) {
    switch (type) {
@@ -82,6 +86,7 @@ __attribute__((unused)) static const char * apir_forward_error(ApirForwardReturn
    APIR_FORWARD_ERROR(APIR_FORWARD_SUCCESS);
    APIR_FORWARD_ERROR(APIR_FORWARD_NO_DISPATCH_FCT);
    APIR_FORWARD_ERROR(APIR_FORWARD_TIMEOUT);
+    APIR_FORWARD_ERROR(APIR_FORWARD_FAILED_TO_SYNC_STREAMS);
    APIR_FORWARD_ERROR(APIR_FORWARD_BASE_INDEX);

    return "Unknown APIR_COMMAND_TYPE_FORWARD error";
@@ -34,3 +34,61 @@ typedef enum ApirBackendCommandType {
    // last command_type index + 1
    APIR_BACKEND_DISPATCH_TABLE_COUNT = 23,
 } ApirBackendCommandType;
+
+static inline const char * apir_dispatch_command_name(ApirBackendCommandType type) {
+    switch (type) {
+        /* device */
+        case APIR_COMMAND_TYPE_DEVICE_GET_DEVICE_COUNT:
+            return "device_get_device_count";
+        case APIR_COMMAND_TYPE_DEVICE_GET_COUNT:
+            return "device_get_count";
+        case APIR_COMMAND_TYPE_DEVICE_GET_NAME:
+            return "device_get_name";
+        case APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION:
+            return "device_get_description";
+        case APIR_COMMAND_TYPE_DEVICE_GET_TYPE:
+            return "device_get_type";
+        case APIR_COMMAND_TYPE_DEVICE_GET_MEMORY:
+            return "device_get_memory";
+        case APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP:
+            return "device_supports_op";
+        case APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE:
+            return "device_get_buffer_type";
+        case APIR_COMMAND_TYPE_DEVICE_GET_PROPS:
+            return "device_get_props";
+        case APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR:
+            return "device_buffer_from_ptr";
+        /* buffer-type */
+        case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME:
+            return "buffer_type_get_name";
+        case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT:
+            return "buffer_type_get_alignment";
+        case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE:
+            return "buffer_type_get_max_size";
+        case APIR_COMMAND_TYPE_BUFFER_TYPE_IS_HOST:
+            return "buffer_type_is_host";
+        case APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER:
+            return "buffer_type_alloc_buffer";
+        case APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE:
+            return "buffer_type_get_alloc_size";
+        /* buffer */
+        case APIR_COMMAND_TYPE_BUFFER_GET_BASE:
+            return "buffer_get_base";
+        case APIR_COMMAND_TYPE_BUFFER_SET_TENSOR:
+            return "buffer_set_tensor";
+        case APIR_COMMAND_TYPE_BUFFER_GET_TENSOR:
+            return "buffer_get_tensor";
+        case APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR:
+            return "buffer_cpy_tensor";
+        case APIR_COMMAND_TYPE_BUFFER_CLEAR:
+            return "buffer_clear";
+        case APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER:
+            return "buffer_free_buffer";
+        /* backend */
+        case APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE:
+            return "backend_graph_compute";
+
+        default:
+            return "unknown";
+    }
+}
@@ -14,7 +14,7 @@
 #define APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED          6
 #define APIR_BACKEND_INITIALIZE_ALREADY_INITED              7
 #define APIR_BACKEND_INITIALIZE_NO_DEVICE                   8
-
+#define APIR_BACKEND_INITIALIZE_BACKEND_INIT_FAILED         9

 // new entries here need to be added to the apir_backend_initialize_error function below

@@ -39,6 +39,10 @@ static const char * apir_backend_initialize_error(int code) {
    APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_MISSING_BACKEND_SYMBOLS);
    APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_MISSING_GGML_SYMBOLS);
    APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_BACKEND_FAILED);
+    APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_BACKEND_REG_FAILED);
+    APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_ALREADY_INITED);
+    APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_NO_DEVICE);
+    APIR_BACKEND_INITIALIZE_ERROR(APIR_BACKEND_INITIALIZE_BACKEND_INIT_FAILED);

    return "Unknown APIR_BACKEND_INITIALIZE error:/";

@@ -13,7 +13,6 @@ struct apir_encoder {
    const char * start;
    const char * end;
    bool         fatal;
-
 };

 struct apir_decoder {
@@ -28,8 +27,8 @@ struct apir_decoder {

 static apir_decoder apir_new_decoder(const char * ptr, size_t size) {
    apir_decoder dec = {
-        .cur = ptr,
-        .end = ptr + size,
+        .cur   = ptr,
+        .end   = ptr + size,
        .fatal = false,
    };

@@ -79,10 +78,7 @@ static inline bool apir_decoder_get_fatal(const apir_decoder * dec) {
 * encode peek
 */

-static inline bool apir_decoder_peek_internal(apir_decoder * dec,
-                                              size_t                size,
-                                              void *                val,
-                                              size_t                val_size) {
+static inline bool apir_decoder_peek_internal(apir_decoder * dec, size_t size, void * val, size_t val_size) {
    assert(val_size <= size);

    if (unlikely(size > (size_t) (dec->end - dec->cur))) {
@@ -332,8 +328,7 @@ static inline void apir_decode_char_array(apir_decoder * dec, char * val, size_t
 static inline void * apir_decoder_alloc_array(size_t size, size_t count) {
    size_t alloc_size;
    if (unlikely(__builtin_mul_overflow(size, count, &alloc_size))) {
-        GGML_LOG_ERROR("%s: overflow in array allocation of %zu * %zu bytes\n",
-                       __func__, size, count);
+        GGML_LOG_ERROR("%s: overflow in array allocation of %zu * %zu bytes\n", __func__, size, count);
        return NULL;
    }

@@ -352,20 +347,19 @@ static inline void apir_decode_bool_t(apir_decoder * dec, bool * val) {

 /* apir_buffer_type_host_handle_t */

-static inline void apir_encode_apir_buffer_type_host_handle_t(apir_encoder *                  enc,
+static inline void apir_encode_apir_buffer_type_host_handle_t(apir_encoder *                         enc,
                                                              const apir_buffer_type_host_handle_t * val) {
    apir_encode(enc, sizeof(apir_buffer_type_host_handle_t), val, sizeof(apir_buffer_type_host_handle_t));
 }

-static inline void apir_decode_apir_buffer_type_host_handle_t(apir_decoder *            dec,
+static inline void apir_decode_apir_buffer_type_host_handle_t(apir_decoder *                   dec,
                                                              apir_buffer_type_host_handle_t * val) {
    apir_decode(dec, sizeof(apir_buffer_type_host_handle_t), val, sizeof(apir_buffer_type_host_handle_t));
 }

 /* apir_buffer_host_handle_t */

-static inline void apir_encode_apir_buffer_host_handle_t(apir_encoder *             enc,
-                                                         const apir_buffer_host_handle_t * val) {
+static inline void apir_encode_apir_buffer_host_handle_t(apir_encoder * enc, const apir_buffer_host_handle_t * val) {
    apir_encode(enc, sizeof(apir_buffer_host_handle_t), val, sizeof(apir_buffer_host_handle_t));
 }

@@ -1,11 +1,10 @@
-#include "ggml-impl.h"
 #include "apir_cs.h"
 #include "apir_cs_rpc.h"
+#include "ggml-impl.h"

 // ggml_buffer_to_apir_host_handle(ggml_backend_buffer_t buffer);

-static inline void apir_encode_ggml_buffer_host_handle(apir_encoder *                    enc,
-                                                       const apir_buffer_host_handle_t * handle);
+static inline void apir_encode_ggml_buffer_host_handle(apir_encoder * enc, const apir_buffer_host_handle_t * handle);

 static inline ggml_backend_buffer_t apir_decode_ggml_buffer(apir_decoder * dec);

@@ -22,8 +21,7 @@ static inline apir_rpc_tensor * apir_decode_apir_rpc_tensor_inplace(apir_decoder
    return (apir_rpc_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, apir_rpc_tensor_size);
 }

-static inline apir_rpc_tensor * apir_decode_apir_rpc_tensor_array_inplace(apir_decoder * dec,
-                                                                          uint32_t       n_tensors) {
+static inline apir_rpc_tensor * apir_decode_apir_rpc_tensor_array_inplace(apir_decoder * dec, uint32_t n_tensors) {
    size_t apir_rpc_tensor_size = sizeof(apir_rpc_tensor) * n_tensors;

    return (apir_rpc_tensor *) (uintptr_t) apir_decoder_use_inplace(dec, apir_rpc_tensor_size);
@@ -45,9 +43,9 @@ static inline const ggml_tensor * apir_decode_ggml_tensor(apir_decoder * dec) {
    }

    ggml_init_params params{
-        /*.mem_size   =*/ ggml_tensor_overhead(),
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
+        /*.mem_size   =*/ggml_tensor_overhead(),
+        /*.mem_buffer =*/NULL,
+        /*.no_alloc   =*/true,
    };

    ggml_context * ctx = ggml_init(params);
@@ -105,6 +103,19 @@ static inline ggml_backend_buffer_t apir_decode_ggml_buffer(apir_decoder * dec)

    apir_decoder_read(dec, buffer_ptr_size, &buffer, buffer_ptr_size);

+    // SECURITY: Validate buffer handle against tracked buffers to prevent
+    // guest VM from providing arbitrary host memory addresses
+    if (buffer) {
+        extern std::unordered_set<ggml_backend_buffer_t> backend_buffers;
+        if (backend_buffers.find(buffer) == backend_buffers.end()) {
+            GGML_LOG_WARN("ggml-virtgpu-backend: %s: Invalid buffer handle from guest: %p\n", __func__,
+                          (void *) buffer);
+            // Set fatal flag to prevent further processing with invalid handle
+            apir_decoder_set_fatal(dec);
+            return NULL;
+        }
+    }
+
    return buffer;
 }

@@ -1,3 +1,6 @@
+#pragma once
+
+// clang-format off
 #include "ggml.h"
 #include "ggml-backend-impl.h"

@@ -5,6 +8,7 @@
 #include <unordered_set>
 #include <vector>
 #include <cstdint>
+// clang-format on

 // ggml_tensor is serialized into apir_rpc_tensor
 struct apir_rpc_tensor {
@@ -34,6 +34,7 @@ static ggml_backend_buffer_t ggml_backend_remoting_buffer_type_alloc_buffer(ggml
 static const char * ggml_backend_remoting_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
    virtgpu * gpu = BUFT_TO_GPU(buft);

+    // Return the prefixed name that was built once during initialization
    return gpu->cached_buffer_type.name;
 }

@@ -53,9 +54,8 @@ static size_t ggml_backend_remoting_buffer_type_get_alloc_size(ggml_backend_buff
                                                               const ggml_tensor *        tensor) {
    virtgpu * gpu = BUFT_TO_GPU(buft);

-    if (tensor->buffer == NULL
-        || !tensor->buffer->context
-        || !buft->device->iface.supports_buft(buft->device, tensor->buffer->buft)) {
+    if (tensor->buffer == NULL || !tensor->buffer->context ||
+        !buft->device->iface.supports_buft(buft->device, tensor->buffer->buft)) {
        return ggml_nbytes(tensor);
    }

@@ -3,6 +3,7 @@
 static const char * ggml_backend_remoting_device_get_name(ggml_backend_dev_t dev) {
    virtgpu * gpu = DEV_TO_GPU(dev);

+    // Return the prefixed name that was built once during initialization
    return gpu->cached_device_info.name;
 }

@@ -22,7 +23,7 @@ static enum ggml_backend_dev_type ggml_backend_remoting_device_get_type(ggml_bac
 static void ggml_backend_remoting_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
    virtgpu * gpu = DEV_TO_GPU(dev);

-    *free = gpu->cached_device_info.memory_free;
+    *free  = gpu->cached_device_info.memory_free;
    *total = gpu->cached_device_info.memory_total;
 }

@@ -72,7 +73,7 @@ static void ggml_backend_remoting_device_get_props(ggml_backend_dev_t dev, ggml_
 ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_backend_dev_t dev) {
    virtgpu * gpu = DEV_TO_GPU(dev);

-    static std::atomic<bool> initialized = false;
+    static std::atomic<bool>        initialized = false;
    static ggml_backend_buffer_type buft;

    if (!initialized) {
@@ -95,7 +96,7 @@ ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_type(ggml_bac
 static ggml_backend_buffer_type_t ggml_backend_remoting_device_get_buffer_from_ptr_type(ggml_backend_dev_t dev) {
    virtgpu * gpu = DEV_TO_GPU(dev);

-    static std::atomic<bool> initialized = false;
+    static std::atomic<bool>        initialized = false;
    static ggml_backend_buffer_type buft;

    if (!initialized) {
@@ -7,8 +7,8 @@
 void ggml_virtgpu_cleanup(virtgpu * gpu);

 static virtgpu * apir_initialize() {
-    static virtgpu *         gpu          = NULL;
-    static std::atomic<bool> initialized  = false;
+    static virtgpu *         gpu         = NULL;
+    static std::atomic<bool> initialized = false;

    if (initialized) {
        // fast track
@@ -31,29 +31,53 @@ static virtgpu * apir_initialize() {
        }

        // Pre-fetch and cache all device information, it will not change
-        gpu->cached_device_info.description  = apir_device_get_description(gpu);
+        gpu->cached_device_info.description = apir_device_get_description(gpu);
        if (!gpu->cached_device_info.description) {
            GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu device description", __func__);
        }
-        gpu->cached_device_info.name         = apir_device_get_name(gpu);
-        if (!gpu->cached_device_info.name) {
-            GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu device name", __func__);
-        }
        gpu->cached_device_info.device_count = apir_device_get_count(gpu);
        gpu->cached_device_info.type         = apir_device_get_type(gpu);

-        apir_device_get_memory(gpu,
-                              &gpu->cached_device_info.memory_free,
-                              &gpu->cached_device_info.memory_total);
+        {
+            // Get the remote name and create prefixed version
+            char * rmt_device_name = apir_device_get_name(gpu);
+            if (!rmt_device_name) {
+                GGML_ABORT(GGML_VIRTGPU "%s: failed to get the virtgpu device name", __func__);
+            }
+
+            size_t device_name_len       = strlen(rmt_device_name) + 11;  // "[virtgpu] " + null terminator
+            gpu->cached_device_info.name = (char *) malloc(device_name_len);
+            if (!gpu->cached_device_info.name) {
+                free(rmt_device_name);
+                GGML_ABORT(GGML_VIRTGPU "%s: failed to allocate memory for prefixed device name", __func__);
+            }
+            snprintf(gpu->cached_device_info.name, device_name_len, "[virtgpu] %s", rmt_device_name);
+            free(rmt_device_name);
+        }
+
+        apir_device_get_memory(gpu, &gpu->cached_device_info.memory_free, &gpu->cached_device_info.memory_total);

        apir_buffer_type_host_handle_t buft_host_handle = apir_device_get_buffer_type(gpu);
        gpu->cached_buffer_type.host_handle             = buft_host_handle;
-        gpu->cached_buffer_type.name                    = apir_buffer_type_get_name(gpu, buft_host_handle);
-        if (!gpu->cached_buffer_type.name) {
-            GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu buffer type name", __func__);
+        {
+            // Get the remote name and create prefixed version
+            char * rmt_name = apir_buffer_type_get_name(gpu, buft_host_handle);
+            if (!rmt_name) {
+                GGML_ABORT(GGML_VIRTGPU "%s: failed to get the virtgpu buffer type name", __func__);
+            }
+
+            size_t prefixed_len          = strlen(rmt_name) + 11;  // "[virtgpu] " + null terminator
+            gpu->cached_buffer_type.name = (char *) malloc(prefixed_len);
+            if (!gpu->cached_buffer_type.name) {
+                free(rmt_name);
+                GGML_ABORT(GGML_VIRTGPU "%s: failed to allocate memory for prefixed buffer type name", __func__);
+            }
+            snprintf(gpu->cached_buffer_type.name, prefixed_len, "[virtgpu] %s", rmt_name);
+            free(rmt_name);
        }
-        gpu->cached_buffer_type.alignment               = apir_buffer_type_get_alignment(gpu, buft_host_handle);
-        gpu->cached_buffer_type.max_size                = apir_buffer_type_get_max_size(gpu, buft_host_handle);
+
+        gpu->cached_buffer_type.alignment = apir_buffer_type_get_alignment(gpu, buft_host_handle);
+        gpu->cached_buffer_type.max_size  = apir_buffer_type_get_max_size(gpu, buft_host_handle);

        initialized = true;
    }
@@ -98,7 +122,7 @@ static void ggml_backend_remoting_reg_init_devices(ggml_backend_reg_t reg) {
    static std::atomic<bool> initialized = false;

    if (initialized) {
-        return; // fast track
+        return;  // fast track
    }

    {
@@ -1,5 +1,5 @@
-#include "ggml-remoting.h"
 #include "../../include/ggml-virtgpu.h"
+#include "ggml-remoting.h"

 static const char * ggml_backend_remoting_get_name(ggml_backend_t backend) {
    UNUSED(backend);
@@ -9,7 +9,7 @@
 #include <string>

 #define GGML_VIRTGPU_NAME "ggml-virtgpu"
-#define GGML_VIRTGPU "ggml-virtgpu: "
+#define GGML_VIRTGPU      "ggml-virtgpu: "

 // USE_ALWAYS_TRUE_SUPPORTS_OP: 1 is fast, 0 avoid micro-benchmark crashes

@@ -3,7 +3,7 @@
 #include <stdint.h>

 struct virgl_renderer_capset_apir {
-   uint32_t apir_version;
-   uint32_t supports_blob_resources;
-   uint32_t reserved[4];           // For future expansion
+    uint32_t apir_version;
+    uint32_t supports_blob_resources;
+    uint32_t reserved[4];  // For future expansion
 };
@@ -145,8 +145,31 @@ class RemotingCodebaseGenerator:
        enum_lines.append(f"  APIR_BACKEND_DISPATCH_TABLE_COUNT = {total_count},")
        enum_lines.append("} ApirBackendCommandType;")

+        # Generate function name mapping
+        func_lines = []
+        func_lines.append("static inline const char * apir_dispatch_command_name(ApirBackendCommandType type) {")
+        func_lines.append("    switch (type) {")
+
+        current_group = None
+        for func in functions:
+            # Add comment for new group
+            if func['group_name'] != current_group:
+                func_lines.append(f"        /* {func['group_description']} */")
+                current_group = func['group_name']
+
+            # Generate clean function name without backend_ prefix
+            clean_name = f"{func['group_name']}_{func['function_name']}"
+            func_lines.append(f"        case {func['enum_name']}:")
+            func_lines.append(f"            return \"{clean_name}\";")
+
+        func_lines.append("")
+        func_lines.append("        default:")
+        func_lines.append("            return \"unknown\";")
+        func_lines.append("    }")
+        func_lines.append("}")
+
        # Full header template
-        header_content = NL.join(enum_lines) + "\n"
+        header_content = NL.join(enum_lines) + "\n\n" + NL.join(func_lines) + "\n"

        return header_content

@@ -170,19 +193,6 @@ class RemotingCodebaseGenerator:

            decl_lines.append(f"{signature} {func['backend_function']}({params});")

-        # Switch cases
-        switch_lines = []
-        current_group = None
-
-        for func in functions:
-            if func['group_name'] != current_group:
-                switch_lines.append(f"  /* {func['group_description']} */")
-                current_group = func['group_name']
-
-            deprecated = " (DEPRECATED)" if func['deprecated'] else ""
-
-            switch_lines.append(f"  case {func['enum_name']}: return \"{func['backend_function']}{deprecated}\";")
-
        # Dispatch table
        table_lines = []
        current_group = None
@@ -201,15 +211,6 @@ class RemotingCodebaseGenerator:

 {NL.join(decl_lines)}

-static inline const char *backend_dispatch_command_name(ApirBackendCommandType type)
-{{
-  switch (type) {{
-{NL.join(switch_lines)}
-
-  default: return "unknown";
-  }}
-}}
-
 extern "C" {{
 static const backend_dispatch_t apir_backend_dispatch_table[APIR_BACKEND_DISPATCH_TABLE_COUNT] = {{
  {NL.join(table_lines)}
@@ -17,8 +17,8 @@ ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) {
    size_t               cgraph_size = apir_serialize_ggml_cgraph(cgraph, cgraph_data);

    virtgpu_shmem   temp_shmem;  // Local storage for large buffers
-    virtgpu_shmem * shmem = &temp_shmem;
-    bool using_shared_shmem = false;
+    virtgpu_shmem * shmem              = &temp_shmem;
+    bool            using_shared_shmem = false;

    if (cgraph_size <= gpu->data_shmem.mmap_size) {
        // Lock mutex before using shared data_shmem buffer
@@ -26,7 +26,7 @@ ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) {
            GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__);
        }
        using_shared_shmem = true;
-        shmem = &gpu->data_shmem;
+        shmem              = &gpu->data_shmem;
    } else if (virtgpu_shmem_create(gpu, cgraph_size, shmem)) {
        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__);
    }
@@ -62,7 +62,9 @@ size_t apir_buffer_type_get_max_size(virtgpu * gpu, apir_buffer_type_host_handle
    return max_size;
 }

-apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle, size_t size) {
+apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu *                      gpu,
+                                                    apir_buffer_type_host_handle_t host_handle,
+                                                    size_t                         size) {
    apir_encoder *        encoder;
    apir_decoder *        decoder;
    ApirForwardReturnCode ret;
@@ -84,7 +86,9 @@ apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, apir_buffer_t
    return buffer_context;
 }

-size_t apir_buffer_type_get_alloc_size(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle, const ggml_tensor * op) {
+size_t apir_buffer_type_get_alloc_size(virtgpu *                      gpu,
+                                       apir_buffer_type_host_handle_t host_handle,
+                                       const ggml_tensor *            op) {
    apir_encoder *        encoder;
    apir_decoder *        decoder;
    ApirForwardReturnCode ret;
@@ -35,8 +35,8 @@ void apir_buffer_set_tensor(virtgpu *               gpu,
    apir_encode_ggml_tensor(encoder, tensor);

    virtgpu_shmem   temp_shmem;  // Local storage for large buffers
-    virtgpu_shmem * shmem = &temp_shmem;
-    bool using_shared_shmem = false;
+    virtgpu_shmem * shmem              = &temp_shmem;
+    bool            using_shared_shmem = false;

    if (size <= gpu->data_shmem.mmap_size) {
        // Lock mutex before using shared data_shmem buffer
@@ -44,7 +44,7 @@ void apir_buffer_set_tensor(virtgpu *               gpu,
            GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__);
        }
        using_shared_shmem = true;
-        shmem = &gpu->data_shmem;
+        shmem              = &gpu->data_shmem;

    } else if (virtgpu_shmem_create(gpu, size, shmem)) {
        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__);
@@ -86,8 +86,8 @@ void apir_buffer_get_tensor(virtgpu *               gpu,
    apir_encode_ggml_tensor(encoder, tensor);

    virtgpu_shmem   temp_shmem;  // Local storage for large buffers
-    virtgpu_shmem * shmem = &temp_shmem;
-    bool using_shared_shmem = false;
+    virtgpu_shmem * shmem              = &temp_shmem;
+    bool            using_shared_shmem = false;

    if (size <= gpu->data_shmem.mmap_size) {
        // Lock mutex before using shared data_shmem buffer
@@ -95,7 +95,7 @@ void apir_buffer_get_tensor(virtgpu *               gpu,
            GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__);
        }
        using_shared_shmem = true;
-        shmem = &gpu->data_shmem;
+        shmem              = &gpu->data_shmem;

    } else if (virtgpu_shmem_create(gpu, size, shmem)) {
        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__);
@@ -26,7 +26,7 @@ char * apir_device_get_name(virtgpu * gpu) {
    REMOTE_CALL(gpu, encoder, decoder, ret);

    const size_t string_size = apir_decode_array_size_unchecked(decoder);
-    char            * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size);
+    char *       string      = (char *) apir_decoder_alloc_array(sizeof(char), string_size);
    if (!string) {
        GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device name buffer\n", __func__);
        return NULL;
@@ -173,7 +173,7 @@ apir_buffer_context_t apir_device_buffer_from_ptr(virtgpu * gpu, size_t size, si
    REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR);

    if (virtgpu_shmem_create(gpu, size, &buffer_context.shmem)) {
-        GGML_ABORT(GGML_VIRTGPU "Couldn't allocate the guest-host shared buffer");
+        GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate %ldb of guest-host shared buffer", __func__, size);
    }

    apir_encode_virtgpu_shmem_res_id(encoder, buffer_context.shmem.res_id);
@@ -1,29 +1,36 @@
-#include "virtgpu.h"
+#pragma once

+// clang-format off
+#include "virtgpu.h"
 #include "ggml-remoting.h"
 #include "backend/shared/apir_backend.h"
 #include "backend/shared/apir_cs_ggml.h"
-
 #include "ggml-backend-impl.h"
+// clang-format on

-#define REMOTE_CALL_PREPARE(gpu_dev_name, encoder_name, apir_command_type__)                               \
-    do {                                                                                                   \
-        int32_t forward_flag = (int32_t) apir_command_type__;                                              \
-        encoder_name         = remote_call_prepare(gpu_dev_name, APIR_COMMAND_TYPE_FORWARD, forward_flag); \
-        if (!encoder_name) {                                                                               \
-            GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__);                       \
-        }                                                                                                  \
+#define REMOTE_CALL_PREPARE(gpu_dev_name, encoder_name, apir_command_type__)                                           \
+    int32_t      REMOTE_CALL_PREPARE_forward_flag = (int32_t) apir_command_type__;                                     \
+    const char * REMOTE_CALL_PREPARE_command_name = apir_dispatch_command_name(apir_command_type__);                   \
+    do {                                                                                                               \
+        encoder_name = remote_call_prepare(gpu_dev_name, APIR_COMMAND_TYPE_FORWARD, REMOTE_CALL_PREPARE_forward_flag); \
+        if (!encoder_name) {                                                                                           \
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__);                        \
+        }                                                                                                              \
    } while (0)

-#define REMOTE_CALL(gpu_dev_name, encoder_name, decoder_name, ret_name)                                           \
-    do {                                                                                                          \
-        ret_name = (ApirForwardReturnCode) remote_call(gpu_dev_name, encoder_name, &decoder_name, 0, NULL);       \
-        if (!decoder_name) {                                                                                      \
-            GGML_ABORT(GGML_VIRTGPU "%s: failed to kick the remote call", __func__);                                         \
-        }                                                                                                         \
-        if (ret_name < APIR_FORWARD_BASE_INDEX) {                                                                 \
-            GGML_ABORT(GGML_VIRTGPU "%s: failed to forward the API call: %s: code %d", __func__,                             \
-                       apir_forward_error(ret_name), ret_name);                                                   \
-        }                                                                                                         \
-        ret_name = (ApirForwardReturnCode) (ret_name - APIR_FORWARD_BASE_INDEX);                                  \
+#define REMOTE_CALL(gpu_dev_name, encoder_name, decoder_name, ret_name)                                     \
+    do {                                                                                                    \
+        ret_name = (ApirForwardReturnCode) remote_call(gpu_dev_name, encoder_name, &decoder_name, 0, NULL); \
+        if (!decoder_name) {                                                                                \
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to kick the remote call", __func__);                        \
+        }                                                                                                   \
+        if (ret_name < APIR_FORWARD_BASE_INDEX) {                                                           \
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to forward the API call: %s: code %d", __func__,            \
+                       apir_forward_error(ret_name), ret_name);                                             \
+        }                                                                                                   \
+        ret_name = (ApirForwardReturnCode) (ret_name - APIR_FORWARD_BASE_INDEX);                            \
+        if (ret_name != 0) {                                                                                \
+            GGML_ABORT(GGML_VIRTGPU "backend function '%s' failed (return code: %d)",                       \
+                       REMOTE_CALL_PREPARE_command_name, ret_name);                                         \
+        }                                                                                                   \
    } while (0)
@@ -20,6 +20,7 @@ apir_buffer_context_t          apir_device_buffer_from_ptr(struct virtgpu * gpu,
 char *                apir_buffer_type_get_name(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
 size_t                apir_buffer_type_get_alignment(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
 size_t                apir_buffer_type_get_max_size(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle);
+/* apir_buffer_type_is_host is deprecated. */
 apir_buffer_context_t apir_buffer_type_alloc_buffer(struct virtgpu *               gpu,
                                                    apir_buffer_type_host_handle_t host_handle,
                                                    size_t                         size);
@@ -53,9 +53,9 @@ static int virtgpu_handshake(virtgpu * gpu) {

    if (!decoder) {
        GGML_ABORT(GGML_VIRTGPU
-            "%s: failed to initiate the communication with the virglrenderer library. "
-            "Most likely, the wrong virglrenderer library was loaded in the hypervisor.",
-            __func__);
+                   "%s: failed to initiate the communication with the virglrenderer library. "
+                   "Most likely, the wrong virglrenderer library was loaded in the hypervisor.",
+                   __func__);
        return 1;
    }

@@ -65,8 +65,7 @@ static int virtgpu_handshake(virtgpu * gpu) {
    uint32_t host_minor;

    if (ret_magic != APIR_HANDSHAKE_MAGIC) {
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: handshake with the virglrenderer failed (code=%d | %s)", __func__, ret_magic,
+        GGML_ABORT(GGML_VIRTGPU "%s: handshake with the virglrenderer failed (code=%d | %s)", __func__, ret_magic,
                   apir_backend_initialize_error(ret_magic));
    } else {
        apir_decode_uint32_t(decoder, &host_major);
@@ -140,15 +139,13 @@ static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) {
                       "Make sure virglrenderer is correctly configured by the hypervisor. (%s) ",
                       __func__, apir_load_library_error(ret));
        } else {
-            GGML_ABORT(GGML_VIRTGPU
-                       "%s: virglrenderer could not load the API Remoting backend library. (%s - code %d)", __func__,
-                       apir_load_library_error(ret), ret);
+            GGML_ABORT(GGML_VIRTGPU "%s: virglrenderer could not load the API Remoting backend library. (%s - code %d)",
+                       __func__, apir_load_library_error(ret), ret);
        }
        return ret;
    }

-    GGML_LOG_INFO(GGML_VIRTGPU
-                  "%s: virglrenderer successfully loaded the API Remoting backend library.\n", __func__);
+    GGML_LOG_INFO(GGML_VIRTGPU "%s: virglrenderer successfully loaded the API Remoting backend library.\n", __func__);

    ApirLoadLibraryReturnCode apir_ret = (ApirLoadLibraryReturnCode) (ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX);

@@ -158,10 +155,11 @@ static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) {
                   "Make sure virglrenderer is correctly configured by the hypervisor. (%s)",
                   __func__, apir_load_library_error(apir_ret));
    } else if (apir_ret == APIR_LOAD_LIBRARY_SYMBOL_MISSING) {
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: the API Remoting backend library couldn't load the GGML backend library, some symbols are missing. "
-                   "Make sure virglrenderer is correctly configured by the hypervisor. (%s)",
-                   __func__, apir_load_library_error(apir_ret));
+        GGML_ABORT(
+            GGML_VIRTGPU
+            "%s: the API Remoting backend library couldn't load the GGML backend library, some symbols are missing. "
+            "Make sure virglrenderer is correctly configured by the hypervisor. (%s)",
+            __func__, apir_load_library_error(apir_ret));
    } else if (apir_ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) {
        GGML_ABORT(GGML_VIRTGPU
                   "%s: the API Remoting backend library couldn't load the GGML backend library: apir code=%d | %s)",
@@ -169,8 +167,8 @@ static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) {
    } else {
        uint32_t lib_ret = apir_ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX;
        GGML_ABORT(GGML_VIRTGPU
-                   "%s: the API Remoting backend library initialize its backend library: apir code=%d)", __func__,
-                   lib_ret);
+                   "%s: the API Remoting backend library failed to initialize its backend library: apir code=%d)",
+                   __func__, lib_ret);
    }
    return ret;
 }
@@ -184,55 +182,49 @@ virtgpu * create_virtgpu() {
    // Initialize mutex to protect shared data_shmem buffer
    if (mtx_init(&gpu->data_shmem_mutex, mtx_plain) != thrd_success) {
        delete gpu;
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: failed to initialize data_shmem mutex", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize data_shmem mutex", __func__);
        return NULL;
    }

    if (virtgpu_open(gpu) != APIR_SUCCESS) {
-        GGML_LOG_ERROR(GGML_VIRTGPU
-                       "%s: failed to open the virtgpu device\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to open the virtgpu device\n", __func__);
        return NULL;
    }

    if (virtgpu_init_capset(gpu) != APIR_SUCCESS) {
        if (gpu->use_apir_capset) {
            GGML_ABORT(GGML_VIRTGPU
-                       "%s: failed to initialize the virtgpu APIR capset. Make sure that the virglrenderer library supports it.", __func__);
+                       "%s: failed to initialize the virtgpu APIR capset. Make sure that the virglrenderer library "
+                       "supports it.",
+                       __func__);
        } else {
-            GGML_ABORT(GGML_VIRTGPU
-                       "%s: failed to initialize the virtgpu Venus capset", __func__);
+            GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu Venus capset", __func__);
        }
        return NULL;
    }

    if (virtgpu_init_context(gpu) != APIR_SUCCESS) {
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: failed to initialize the GPU context", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the GPU context", __func__);
        return NULL;
    }

    if (virtgpu_shmem_create(gpu, SHMEM_REPLY_SIZE, &gpu->reply_shmem)) {
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: failed to create the shared reply memory pages", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: failed to create the shared reply memory pages", __func__);
        return NULL;
    }

    if (virtgpu_shmem_create(gpu, SHMEM_DATA_SIZE, &gpu->data_shmem)) {
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: failed to create the shared data memory pages", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: failed to create the shared data memory pages", __func__);
        return NULL;
    }

    if (virtgpu_handshake(gpu)) {
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: failed to handshake with the virglrenderer library", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: failed to handshake with the virglrenderer library", __func__);
        return NULL;
    }

    if (virtgpu_load_library(gpu) != APIR_LOAD_LIBRARY_SUCCESS) {
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: failed to load the backend library", __func__);
+        GGML_ABORT(GGML_VIRTGPU "%s: failed to load the backend library", __func__);
        return NULL;
    }

@@ -243,8 +235,7 @@ static virt_gpu_result_t virtgpu_open(virtgpu * gpu) {
    drmDevicePtr devs[8];
    int          count = drmGetDevices2(0, devs, ARRAY_SIZE(devs));
    if (count < 0) {
-        GGML_LOG_ERROR(GGML_VIRTGPU
-                       "%s: failed to enumerate DRM devices\n", __func__);
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to enumerate DRM devices\n", __func__);
        return APIR_ERROR_INITIALIZATION_FAILED;
    }

@@ -266,19 +257,17 @@ static virt_gpu_result_t virtgpu_open_device(virtgpu * gpu, const drmDevicePtr d

    int fd = open(node_path, O_RDWR | O_CLOEXEC);
    if (fd < 0) {
-        GGML_ABORT(GGML_VIRTGPU
-                   "%s: failed to open %s", __func__, node_path);
+        GGML_ABORT(GGML_VIRTGPU "%s: failed to open %s", __func__, node_path);
        return APIR_ERROR_INITIALIZATION_FAILED;
    }

    drmVersionPtr version = drmGetVersion(fd);
    if (!version || strcmp(version->name, "virtio_gpu") || version->version_major != 0) {
        if (version) {
-            GGML_LOG_ERROR(GGML_VIRTGPU
-                           "%s: unknown DRM driver %s version %d\n", __func__, version->name, version->version_major);
+            GGML_LOG_ERROR(GGML_VIRTGPU "%s: unknown DRM driver %s version %d\n", __func__, version->name,
+                           version->version_major);
        } else {
-            GGML_LOG_ERROR(GGML_VIRTGPU
-                           "%s: failed to get DRM driver version\n", __func__);
+            GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to get DRM driver version\n", __func__);
        }

        if (version) {
@@ -322,9 +311,8 @@ static virt_gpu_result_t virtgpu_init_capset(virtgpu * gpu) {
        virtgpu_ioctl_get_caps(gpu, gpu->capset.id, gpu->capset.version, &gpu->capset.data, sizeof(gpu->capset.data));

    if (ret) {
-        GGML_LOG_ERROR(GGML_VIRTGPU
-                       "%s: failed to get APIR v%d capset: %s\n",
-                       __func__, gpu->capset.version, strerror(errno));
+        GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to get APIR v%d capset: %s\n", __func__, gpu->capset.version,
+                       strerror(errno));
        return APIR_ERROR_INITIALIZATION_FAILED;
    }

@@ -547,13 +535,10 @@ static void log_call_duration(long long call_duration_ns, const char * name) {
    double call_duration_s  = (double) call_duration_ns / 1e9;  // 1 second = 1e9 nanoseconds

    if (call_duration_s > 1) {
-        GGML_LOG_INFO(GGML_VIRTGPU
-                      "waited %.2fs for the %s host reply...\n", call_duration_s, name);
+        GGML_LOG_INFO(GGML_VIRTGPU "waited %.2fs for the %s host reply...\n", call_duration_s, name);
    } else if (call_duration_ms > 1) {
-        GGML_LOG_INFO(GGML_VIRTGPU
-                      "waited %.2fms for the %s host reply...\n", call_duration_ms, name);
+        GGML_LOG_INFO(GGML_VIRTGPU "waited %.2fms for the %s host reply...\n", call_duration_ms, name);
    } else {
-        GGML_LOG_INFO(GGML_VIRTGPU
-                      "waited %lldns for the %s host reply...\n", call_duration_ns, name);
+        GGML_LOG_INFO(GGML_VIRTGPU "waited %lldns for the %s host reply...\n", call_duration_ns, name);
    }
 }
@@ -1,5 +1,6 @@
 #pragma once

+// clang-format off
 #include "virtgpu-utils.h"
 #include "virtgpu-shm.h"
 #include "virtgpu-apir.h"
@@ -23,20 +24,21 @@
 #include "apir_hw.h"
 #include <drm/virtgpu_drm.h>
 #include "venus_hw.h"
+// clang-format on

 #ifndef VIRTGPU_DRM_CAPSET_APIR
 // Will be defined include/drm/virtgpu_drm.h when
 // https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590/diffs
 // is merged
-#define VIRTGPU_DRM_CAPSET_APIR 10
+#    define VIRTGPU_DRM_CAPSET_APIR 10
 #endif

 // Mesa/Virlgrenderer Venus internal. Only necessary during the
 // Venus->APIR transition in Virglrenderer
 #define VENUS_COMMAND_TYPE_LENGTH 331

-#ifndef VIRTGPU_DRM_CAPSET_VENUS // only available with Linux >= v6.16
-#define VIRTGPU_DRM_CAPSET_VENUS 4
+#ifndef VIRTGPU_DRM_CAPSET_VENUS  // only available with Linux >= v6.16
+#    define VIRTGPU_DRM_CAPSET_VENUS 4
 #endif

 typedef uint32_t virgl_renderer_capset;
@@ -624,8 +624,6 @@ struct vk_device_struct {
    // floor(log2(maxComputeWorkGroupInvocations))
    uint32_t max_workgroup_size_log2 {};

-    bool flash_attention_fp16;
-
    bool coopmat_support;
    bool coopmat_acc_f32_support {};
    bool coopmat_acc_f16_support {};
@@ -2978,11 +2976,15 @@ static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_
    }
 }

-static vk_fa_pipeline_state get_fa_pipeline_state(const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool aligned, bool f32acc,
+static vk_fa_pipeline_state get_fa_pipeline_state(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool aligned, bool f32acc,
                                                  bool use_mask, bool use_mask_opt, bool use_logit_softcap) {
+    const bool old_amd_windows = device->vendor_id == VK_VENDOR_ID_AMD && device->driver_id == vk::DriverId::eAmdProprietary &&
+                                 (device->architecture == AMD_GCN || device->architecture == AMD_RDNA1 || device->architecture == AMD_RDNA2);
+
    uint32_t flags = (use_mask_opt      ? 1 : 0) |
                     (use_mask          ? 2 : 0) |
-                     (use_logit_softcap ? 4 : 0);
+                     (use_logit_softcap ? 4 : 0) |
+                     (old_amd_windows   ? 8 : 0);

    const uint32_t subgroup_size = params.disable_subgroups ? 0 : params.subgroup_size;

@@ -3384,7 +3386,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
            } \
        }

-    if (device->flash_attention_fp16) {
+    if (device->fp16) {
        CREATE_FA(GGML_TYPE_F32, f32, FA_SCALAR, )
        CREATE_FA(GGML_TYPE_F16, f16, FA_SCALAR, )
        CREATE_FA(GGML_TYPE_Q4_0, q4_0, FA_SCALAR, )
@@ -5423,10 +5425,6 @@ static vk_device ggml_vk_get_device(size_t idx) {
            device->mmvq_mode = 1;
        }

-        // Driver issues with older AMD GPUs on Windows, see https://github.com/ggml-org/llama.cpp/pull/19625#issuecomment-3940840613
-        const bool is_amd_proprietary_gcn = device->vendor_id == VK_VENDOR_ID_AMD && device->architecture == AMD_GCN && device->driver_id == vk::DriverId::eAmdProprietary;
-        device->flash_attention_fp16 = device->fp16 && !is_amd_proprietary_gcn;
-
        return device;
    }

@@ -8567,7 +8565,7 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con
    const uint32_t Br = params.block_rows;
    const uint32_t Bc = params.block_cols;

-    const uint32_t float_type_size = device->flash_attention_fp16 ? sizeof(ggml_fp16_t) : sizeof(float);
+    const uint32_t float_type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float);

    // tmpsh is overestimated slightly
    const uint32_t tmpsh = wg_size * sizeof(float);
@@ -8690,7 +8688,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
    uint32_t workgroups_y = (uint32_t)neq2;
    uint32_t workgroups_z = (uint32_t)neq3;

-    const bool f32acc = !ctx->device->flash_attention_fp16 || dst->op_params[3] == GGML_PREC_F32;
+    const bool f32acc = !ctx->device->fp16 || dst->op_params[3] == GGML_PREC_F32;

    // For scalar/coopmat1 FA, we can use the "large" size to accommodate qga.
    // For coopmat2 FA, we always use the small size (which is still pretty large for gqa).
@@ -8745,7 +8743,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx

    // Only use mask opt when the mask is fairly large. This hasn't been tuned extensively.
    bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768;
-    vk_fa_pipeline_state fa_pipeline_state = get_fa_pipeline_state(tuning_params, HSK, HSV, aligned, f32acc,
+    vk_fa_pipeline_state fa_pipeline_state = get_fa_pipeline_state(ctx->device, tuning_params, HSK, HSV, aligned, f32acc,
                                                                   mask != nullptr, use_mask_opt, logit_softcap != 0);

    vk_pipeline pipeline = nullptr;
@@ -465,7 +465,14 @@ void main() {

            if (SubGroupSize > 0) {
                [[unroll]] for (uint s = D_split; s < SubGroupSize; s *= 2) {
-                    Of[r][d] += subgroupShuffleXor(Of[r][d], s);
+                    if (!OLD_AMD_WINDOWS) {
+                        Of[r][d] += subgroupShuffleXor(Of[r][d], s);
+                    } else {
+                        // Something about f16vec4 subgroupShuffleXor is broken on AMD Windows RDNA2 and below.
+                        // Shuffle full vec4 as workaround.
+                        // See https://github.com/ggml-org/llama.cpp/issues/19881#issuecomment-3958643697
+                        Of[r][d] += FLOAT_TYPEV4(subgroupShuffleXor(vec4(Of[r][d]), s));
+                    }
                }
                if (row_split == 1) {
                    barrier();
@@ -14,9 +14,10 @@ layout (constant_id =  9) const uint32_t SHMEM_STAGING = 0;
 layout (constant_id = 10) const uint32_t Flags = 0;
 layout (constant_id = 11) const uint32_t LIMIT_OCCUPANCY_SHMEM = 0;

-const bool USE_MASK_OPT  = (Flags & 1) != 0;
-const bool MASK_ENABLE   = (Flags & 2) != 0;
-const bool LOGIT_SOFTCAP = (Flags & 4) != 0;
+const bool USE_MASK_OPT    = (Flags & 1) != 0;
+const bool MASK_ENABLE     = (Flags & 2) != 0;
+const bool LOGIT_SOFTCAP   = (Flags & 4) != 0;
+const bool OLD_AMD_WINDOWS = (Flags & 8) != 0;

 // Round up head sizes to a multiple of 16, for coopmat1/coopmat2 paths
 const uint32_t HSK_pad = (HSK + 15) & ~15;
@@ -1,12 +1,19 @@
 ggml_add_backend_library(ggml-zendnn
                         ggml-zendnn.cpp)

-# Get ZenDNN path
 if (NOT DEFINED ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "")
    set(ZENDNN_ROOT "$ENV{ZENDNN_ROOT}")
 endif()

-# Check if path is still empty or OFF
+if (BUILD_SHARED_LIBS)
+    set(ZENDNN_SHARED_LIB ON)
+    set(ZENDNN_ARCHIVE_LIB OFF)
+else()
+    set(ZENDNN_SHARED_LIB OFF)
+    set(ZENDNN_ARCHIVE_LIB ON)
+endif()
+
+# Download and build ZenDNN if not provided
 if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
    message(STATUS "ZENDNN_ROOT not set. Automatically downloading and building ZenDNN...")
    message(STATUS "This will take several minutes on first build...")
@@ -21,7 +28,7 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
    ExternalProject_Add(
        zendnn
        GIT_REPOSITORY https://github.com/amd/ZenDNN.git
-        GIT_TAG 21ce8f7879c86bf3637f707fae6f29e0951db5fe
+        GIT_TAG a18adf8c605fb5f5e52cefd7eda08a7b18febbaf    # ZenDNN-2026-WW08
        PREFIX      ${ZENDNN_PREFIX}
        SOURCE_DIR  ${ZENDNN_SOURCE_DIR}
        BINARY_DIR  ${ZENDNN_BUILD_DIR}
@@ -32,7 +39,9 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
            -DZENDNNL_BUILD_DOXYGEN=OFF
            -DZENDNNL_BUILD_GTEST=OFF
            -DZENDNNL_BUILD_BENCHDNN=OFF
-            # Enable ALL matmul algorithm backends
+            -DZENDNNL_DEPENDS_FBGEMM=OFF
+            -DZENDNNL_LIB_BUILD_ARCHIVE=${ZENDNN_ARCHIVE_LIB}
+            -DZENDNNL_LIB_BUILD_SHARED=${ZENDNN_SHARED_LIB}
            -DZENDNNL_DEPENDS_AOCLDLP=ON
            -DZENDNNL_DEPENDS_ONEDNN=ON
            -DZENDNNL_DEPENDS_LIBXSMM=ON
@@ -45,47 +54,37 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
        LOG_INSTALL ON
    )

-    # Add dependency so ZenDNN builds before our library
    add_dependencies(ggml-zendnn zendnn)
-
-    # Set ZENDNN_ROOT to the installation directory
    set(ZENDNN_ROOT ${ZENDNN_INSTALL_DIR})
-
    message(STATUS "ZenDNN will be built to: ${ZENDNN_ROOT}")
 else()
    message(STATUS "Using custom ZenDNN installation at: ${ZENDNN_ROOT}")
 endif()

-# ZenDNN headers + libs
 target_include_directories(ggml-zendnn PRIVATE
    ${ZENDNN_ROOT}/zendnnl/include
-    ${ZENDNN_ROOT}/deps/aocldlp/include
-    ${ZENDNN_ROOT}/deps/aoclutils/include
    ${ZENDNN_ROOT}/deps/json/include
-    ${ZENDNN_ROOT}/deps/libxsmm/include
+    ${ZENDNN_ROOT}/deps/aoclutils/include
+    ${ZENDNN_ROOT}/deps/aocldlp/include
    ${ZENDNN_ROOT}/deps/onednn/include
-)
+    ${ZENDNN_ROOT}/deps/libxsmm/include)

-target_link_directories(ggml-zendnn PRIVATE
-    ${ZENDNN_ROOT}/zendnnl/lib
-    ${ZENDNN_ROOT}/deps/aocldlp/lib
-    ${ZENDNN_ROOT}/deps/aoclutils/lib
-    ${ZENDNN_ROOT}/deps/libxsmm/lib
-    ${ZENDNN_ROOT}/deps/onednn/lib
-)
+if (ZENDNN_SHARED_LIB)
+    target_link_directories(ggml-zendnn PRIVATE ${ZENDNN_ROOT}/zendnnl/lib)
+    target_link_libraries(ggml-zendnn PRIVATE zendnnl)
+elseif (ZENDNN_ARCHIVE_LIB)
+    target_link_libraries(ggml-zendnn PRIVATE
+        ${ZENDNN_ROOT}/zendnnl/lib/libzendnnl_archive.a
+        ${ZENDNN_ROOT}/deps/aoclutils/${CMAKE_INSTALL_LIBDIR}/libaoclutils.a
+        ${ZENDNN_ROOT}/deps/aoclutils/${CMAKE_INSTALL_LIBDIR}/libau_cpuid.a
+        ${ZENDNN_ROOT}/deps/aocldlp/lib/libaocl-dlp.a
+        ${ZENDNN_ROOT}/deps/onednn/${CMAKE_INSTALL_LIBDIR}/libdnnl.a
+        ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmm.a
+        ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmmext.a
+        ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmmnoblas.a)
+endif()

-target_link_libraries(ggml-zendnn PRIVATE
-    zendnnl_archive    # ZenDNN main
-    aocl-dlp           # AOCL libraries
-    aoclutils
-    au_cpuid
-    dnnl               # OneDNN
-    xsmm               # libxsmm small matrix math
-    xsmmext
-    xsmmnoblas
-    m
-    pthread
-)
+target_link_libraries(ggml-zendnn PRIVATE m pthread)

 if (GGML_OPENMP)
    target_link_libraries(ggml-zendnn PRIVATE OpenMP::OpenMP_CXX)
@@ -41,13 +41,13 @@ static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int
                               const TA * A, int64_t lda, const TB * B, int64_t ldb, TC * C,
                               int64_t ldc) {

-    zendnnl::lowoha::lowoha_params params;
+    zendnnl::lowoha::matmul::matmul_params params;
    params.dtypes.src = ggml_to_zendnn_type<TB>();
    params.dtypes.wei = ggml_to_zendnn_type<TA>();
    params.dtypes.dst = ggml_to_zendnn_type<TC>();
    params.num_threads = ctx->n_threads;

-    zendnnl::lowoha::status_t status = zendnnl::lowoha::matmul_direct(
+    zendnnl::error_handling::status_t status = zendnnl::lowoha::matmul::matmul_direct(
        'r', false, true,   // row-major, don't transpose B, transpose A (because it's column-major)
        n,                  // M: rows of B and C
        m,                  // N: cols of A^T and C
@@ -63,7 +63,7 @@ static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int
        params              // params
    );

-    if (status != zendnnl::lowoha::status_t::success) {
+    if (status != zendnnl::error_handling::status_t::success) {
        GGML_LOG_ERROR("%s, ZenDNN matmul failed: status=%d\n", __func__, static_cast<int>(status));
        return false;
    }
@@ -228,13 +228,41 @@ struct gguf_context {
 };

 struct gguf_reader {
-    FILE * file;
+    gguf_reader(FILE * file) : file(file) {
+        // read the remaining bytes once and update on each read
+        nbytes_remain = file_remain(file);
+    }

-    gguf_reader(FILE * file) : file(file) {}
+    // helper for remaining bytes in a file
+    static uint64_t file_remain(FILE * file) {
+        const int64_t cur = gguf_ftell(file);
+        if (cur < 0) {
+            return 0;
+        }
+        if (gguf_fseek(file, 0, SEEK_END) != 0) {
+            gguf_fseek(file, cur, SEEK_SET);
+
+            return 0;
+        }
+        const int64_t end = gguf_ftell(file);
+        if (end < 0) {
+            gguf_fseek(file, cur, SEEK_SET);
+
+            return 0;
+        }
+        gguf_fseek(file, cur, SEEK_SET);
+        return static_cast<uint64_t>(end - cur);
+    }

    template <typename T>
    bool read(T & dst) const {
-        return fread(&dst, 1, sizeof(dst), file) == sizeof(dst);
+        const size_t size = sizeof(dst);
+        if (nbytes_remain < size) {
+            return false;
+        }
+        const size_t nread = fread(&dst, 1, size, file);
+        nbytes_remain -= nread;
+        return nread == size;
    }

    template <typename T>
@@ -242,20 +270,19 @@ struct gguf_reader {
        if (n > GGUF_MAX_ARRAY_ELEMENTS) {
            return false;
        }
-        const uint64_t nbytes = nbytes_remain();
        if constexpr (std::is_same<T, std::string>::value) {
            // strings are prefixed with their length, so we need to account for that
            if (n > SIZE_MAX / sizeof(uint64_t)) {
                return false;
            }
-            if (nbytes < n * sizeof(uint64_t)) {
+            if (nbytes_remain < n * sizeof(uint64_t)) {
                return false;
            }
        } else {
            if (n > SIZE_MAX / sizeof(T)) {
                return false;
            }
-            if (nbytes < n * sizeof(T)) {
+            if (nbytes_remain < n * sizeof(T)) {
                return false;
            }
        }
@@ -312,39 +339,29 @@ struct gguf_reader {
            GGML_LOG_ERROR("%s: string length %" PRIu64 " exceeds maximum %" PRIu64 "\n", __func__, size, (uint64_t) GGUF_MAX_STRING_LENGTH);
            return false;
        }
-        const uint64_t nbytes = nbytes_remain();
-        if (size > nbytes) {
-            GGML_LOG_ERROR("%s: string length %" PRIu64 " exceeds remaining file size %" PRIu64 " bytes\n", __func__, size, nbytes);
+        if (size > nbytes_remain) {
+            GGML_LOG_ERROR("%s: string length %" PRIu64 " exceeds remaining file size %" PRIu64 " bytes\n", __func__, size, nbytes_remain);
            return false;
        }
        dst.resize(static_cast<size_t>(size));
-        return fread(dst.data(), 1, dst.length(), file) == dst.length();
+        const size_t nread = fread(dst.data(), 1, size, file);
+        nbytes_remain -= nread;
+        return nread == size;
    }

    bool read(void * dst, const size_t size) const {
-        return fread(dst, 1, size, file) == size;
+        if (size > nbytes_remain) {
+            return false;
+        }
+        const size_t nread = fread(dst, 1, size, file);
+        nbytes_remain -= nread;
+        return nread == size;
    }

-    // remaining bytes in the file
-    uint64_t nbytes_remain() const {
-        const int64_t cur = gguf_ftell(file);
-        if (cur < 0) {
-            return 0;
-        }
-        if (gguf_fseek(file, 0, SEEK_END) != 0) {
-            gguf_fseek(file, cur, SEEK_SET);
+private:
+    FILE * file;

-            return 0;
-        }
-        const int64_t end = gguf_ftell(file);
-        if (end < 0) {
-            gguf_fseek(file, cur, SEEK_SET);
-
-            return 0;
-        }
-        gguf_fseek(file, cur, SEEK_SET);
-        return static_cast<uint64_t>(end - cur);
-    }
+    mutable uint64_t nbytes_remain;
 };

 struct gguf_context * gguf_init_empty(void) {
@@ -379,6 +379,7 @@ class MODEL_ARCH(IntEnum):
    NEO_BERT         = auto()
    JINA_BERT_V2     = auto()
    JINA_BERT_V3     = auto()
+    EUROBERT         = auto()
    BLOOM            = auto()
    STABLELM         = auto()
    QWEN             = auto()
@@ -531,6 +532,7 @@ class MODEL_TENSOR(IntEnum):
    FFN_GATE_EXP         = auto()
    FFN_DOWN_EXP         = auto()
    FFN_UP_EXP           = auto()
+    FFN_GATE_UP_EXP      = auto()
    FFN_GATE_SHEXP       = auto()
    FFN_DOWN_SHEXP       = auto()
    FFN_UP_SHEXP         = auto()
@@ -820,6 +822,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.NEO_BERT:         "neo-bert",
    MODEL_ARCH.JINA_BERT_V2:     "jina-bert-v2",
    MODEL_ARCH.JINA_BERT_V3:     "jina-bert-v3",
+    MODEL_ARCH.EUROBERT:         "eurobert",
    MODEL_ARCH.BLOOM:            "bloom",
    MODEL_ARCH.STABLELM:         "stablelm",
    MODEL_ARCH.QWEN:             "qwen",
@@ -978,6 +981,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.FFN_GATE_EXP:              "blk.{bid}.ffn_gate_exps",
    MODEL_TENSOR.FFN_DOWN_EXP:              "blk.{bid}.ffn_down_exps",
    MODEL_TENSOR.FFN_UP_EXP:                "blk.{bid}.ffn_up_exps",
+    MODEL_TENSOR.FFN_GATE_UP_EXP:           "blk.{bid}.ffn_gate_up_exps",
    MODEL_TENSOR.FFN_EXP_PROBS_B:           "blk.{bid}.exp_probs_b",
    MODEL_TENSOR.LAYER_OUT_NORM:            "blk.{bid}.layer_output_norm",
    MODEL_TENSOR.PER_LAYER_TOKEN_EMBD:      "per_layer_token_embd",           # gemma3n
@@ -1587,6 +1591,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_UP,
        MODEL_TENSOR.LAYER_OUT_NORM,
    ],
+    MODEL_ARCH.EUROBERT: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_DOWN,
+    ],
    MODEL_ARCH.MPT: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
@@ -1805,6 +1822,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_DOWN_EXP,
        MODEL_TENSOR.FFN_UP_EXP,
        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_GATE_UP_EXP,
        MODEL_TENSOR.SSM_A,
        MODEL_TENSOR.SSM_CONV1D,
        MODEL_TENSOR.SSM_DT,
@@ -1894,6 +1912,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_DOWN_EXP,
        MODEL_TENSOR.FFN_UP_EXP,
        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_GATE_UP_EXP,
        MODEL_TENSOR.SSM_A,
        MODEL_TENSOR.SSM_CONV1D,
        MODEL_TENSOR.SSM_DT,
@@ -2595,6 +2614,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_GATE_EXP,
        MODEL_TENSOR.FFN_DOWN_EXP,
        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_UP_EXP,
        MODEL_TENSOR.FFN_GATE_SHEXP,
        MODEL_TENSOR.FFN_DOWN_SHEXP,
        MODEL_TENSOR.FFN_UP_SHEXP,
@@ -567,6 +567,10 @@ class TensorNameMap:
            "model.layers.{bid}.mlp.chunk_experts.gate_proj",           # grovemoe
        ),

+        MODEL_TENSOR.FFN_GATE_UP_EXP: (
+            "model.layers.{bid}.mlp.experts.gate_up_proj",
+        ),
+
        # Feed-forward down
        MODEL_TENSOR.FFN_DOWN: (
            "gpt_neox.layers.{bid}.mlp.dense_4h_to_h",                # gptneox
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.17.1"
+version = "0.18.0"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [
@@ -62,6 +62,7 @@ add_library(llama
            models/dream.cpp
            models/ernie4-5-moe.cpp
            models/ernie4-5.cpp
+            models/eurobert.cpp
            models/exaone-moe.cpp
            models/exaone.cpp
            models/exaone4.cpp
@@ -26,6 +26,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
    { LLM_ARCH_NEO_BERT,         "neo-bert"         },
    { LLM_ARCH_JINA_BERT_V2,     "jina-bert-v2"     },
    { LLM_ARCH_JINA_BERT_V3,     "jina-bert-v3"     },
+    { LLM_ARCH_EUROBERT,         "eurobert"         },
    { LLM_ARCH_BLOOM,            "bloom"            },
    { LLM_ARCH_STABLELM,         "stablelm"         },
    { LLM_ARCH_QWEN,             "qwen"             },
@@ -348,6 +349,7 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
    { LLM_TENSOR_FFN_DOWN_EXP,                           "blk.%d.ffn_down.%d" },
    { LLM_TENSOR_FFN_UP_EXP,                             "blk.%d.ffn_up.%d" },
    { LLM_TENSOR_FFN_GATE_EXPS,                          "blk.%d.ffn_gate_exps" },
+    { LLM_TENSOR_FFN_GATE_UP_EXPS,                       "blk.%d.ffn_gate_up_exps" },
    { LLM_TENSOR_FFN_DOWN_EXPS,                          "blk.%d.ffn_down_exps" },
    { LLM_TENSOR_FFN_UP_EXPS,                            "blk.%d.ffn_up_exps" },
    { LLM_TENSOR_ATTN_POST_NORM,                         "blk.%d.post_attention_norm" },
@@ -819,6 +821,20 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_CLS,
                LLM_TENSOR_CLS_OUT,
            };
+        case LLM_ARCH_EUROBERT:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_DOWN,
+            };
        case LLM_ARCH_MODERN_BERT:
            return {
                LLM_TENSOR_TOKEN_EMBD,
@@ -989,6 +1005,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_FFN_GATE_EXPS,
                LLM_TENSOR_FFN_DOWN_EXPS,
                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_UP_EXPS,
                LLM_TENSOR_FFN_GATE_INP_SHEXP,
                LLM_TENSOR_FFN_GATE_SHEXP,
                LLM_TENSOR_FFN_DOWN_SHEXP,
@@ -1046,6 +1063,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_FFN_GATE_EXPS,
                LLM_TENSOR_FFN_DOWN_EXPS,
                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_UP_EXPS,
                LLM_TENSOR_FFN_GATE_INP_SHEXP,
                LLM_TENSOR_FFN_GATE_SHEXP,
                LLM_TENSOR_FFN_DOWN_SHEXP,
@@ -1586,6 +1604,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_FFN_GATE_EXPS,
                LLM_TENSOR_FFN_DOWN_EXPS,
                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_UP_EXPS,
                LLM_TENSOR_FFN_GATE_INP_SHEXP,
                LLM_TENSOR_FFN_GATE_SHEXP,
                LLM_TENSOR_FFN_DOWN_SHEXP,
@@ -2670,6 +2689,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
    {LLM_TENSOR_FFN_DOWN_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
    {LLM_TENSOR_FFN_GATE_EXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
    {LLM_TENSOR_FFN_UP_EXPS,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+    {LLM_TENSOR_FFN_GATE_UP_EXPS,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
    {LLM_TENSOR_FFN_DOWN_CHEXPS,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
    {LLM_TENSOR_FFN_GATE_CHEXPS,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
    {LLM_TENSOR_FFN_UP_CHEXPS,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
@@ -30,6 +30,7 @@ enum llm_arch {
    LLM_ARCH_NEO_BERT,
    LLM_ARCH_JINA_BERT_V2,
    LLM_ARCH_JINA_BERT_V3,
+    LLM_ARCH_EUROBERT,
    LLM_ARCH_BLOOM,
    LLM_ARCH_STABLELM,
    LLM_ARCH_QWEN,
@@ -372,6 +373,7 @@ enum llm_tensor {
    LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
    LLM_TENSOR_FFN_GATE_EXPS,
    LLM_TENSOR_FFN_UP_EXPS,
+    LLM_TENSOR_FFN_GATE_UP_EXPS,
    LLM_TENSOR_FFN_DOWN_SHEXP,
    LLM_TENSOR_FFN_GATE_SHEXP,
    LLM_TENSOR_FFN_UP_SHEXP,
@@ -1165,7 +1165,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
               float   w_scale,
         llama_expert_gating_func_type gating_op,
                 int   il,
-         ggml_tensor * probs_in) const {
+         ggml_tensor * probs_in,
+         ggml_tensor * gate_up_exps) const {
    return build_moe_ffn(
        cur,
        gate_inp,  /* gate_inp_b  */ nullptr,
@@ -1181,7 +1182,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
        w_scale,
        gating_op,
        il,
-        probs_in
+        probs_in,
+        gate_up_exps
    );
 }

@@ -1204,7 +1206,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
               float   w_scale,
        llama_expert_gating_func_type gating_op,
                 int   il,
-         ggml_tensor * probs_in) const {
+         ggml_tensor * probs_in,
+         ggml_tensor * gate_up_exps,
+         ggml_tensor * gate_up_exps_b) const {
    const int64_t n_embd   = cur->ne[0];
    const int64_t n_tokens = cur->ne[1];
    const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN
@@ -1343,26 +1347,48 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
        cb(cur, "ffn_moe_weighted", il);
    }

-    ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
-    cb(up, "ffn_moe_up", il);
-
-    if (up_exps_b) {
-        up = ggml_add_id(ctx0, up, up_exps_b, selected_experts);
-        cb(up, "ffn_moe_up_biased", il);
-    }
-
+    ggml_tensor * up = nullptr;
    ggml_tensor * experts = nullptr;
-    if (gate_exps) {
-        cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+
+    if (gate_up_exps) {
+        // merged gate_up path: one mul_mat_id, then split into gate and up views
+        ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts); // [n_ff*2, n_expert_used, n_tokens]
+        cb(gate_up, "ffn_moe_gate_up", il);
+
+        if (gate_up_exps_b) {
+            gate_up = ggml_add_id(ctx0, gate_up, gate_up_exps_b, selected_experts);
+            cb(gate_up, "ffn_moe_gate_up_biased", il);
+        }
+
+        const int64_t n_ff = gate_up->ne[0] / 2;
+        cur = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], 0);
        cb(cur, "ffn_moe_gate", il);
+        up  = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], n_ff * gate_up->nb[0]);
+        cb(up, "ffn_moe_up", il);
    } else {
-        cur = up;
+        // separate gate and up path
+        up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+        cb(up, "ffn_moe_up", il);
+
+        if (up_exps_b) {
+            up = ggml_add_id(ctx0, up, up_exps_b, selected_experts);
+            cb(up, "ffn_moe_up_biased", il);
+        }
+
+        if (gate_exps) {
+            cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+            cb(cur, "ffn_moe_gate", il);
+        } else {
+            cur = up;
+        }
+
+        if (gate_exps_b) {
+            cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
+            cb(cur, "ffn_moe_gate_biased", il);
+        }
    }

-    if (gate_exps_b) {
-        cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
-        cb(cur, "ffn_moe_gate_biased", il);
-    }
+    const bool has_gate = gate_exps || gate_up_exps;

    switch (type_op) {
        case LLM_FFN_SILU:
@@ -1385,7 +1411,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
                        break;
                    }
                }
+            }

+            if (has_gate) {
                cur = ggml_swiglu_split(ctx0, cur, up);
                cb(cur, "ffn_moe_swiglu", il);
            } else {
@@ -1393,7 +1421,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
                cb(cur, "ffn_moe_silu", il);
            } break;
        case LLM_FFN_GELU:
-            if (gate_exps) {
+            if (has_gate) {
                cur = ggml_geglu_split(ctx0, cur, up);
                cb(cur, "ffn_moe_geglu", il);
            } else {
@@ -1409,7 +1437,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
                cb(cur, "ffn_moe_swiglu_oai", il);
            } break;
        case LLM_FFN_RELU:
-            if (gate_exps) {
+            if (has_gate) {
                cur = ggml_reglu_split(ctx0, cur, up);
                cb(cur, "ffn_moe_reglu", il);
            } else {
@@ -1417,7 +1445,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
                cb(cur, "ffn_moe_relu", il);
            } break;
        case LLM_FFN_RELU_SQR:
-            if (gate_exps) {
+            if (has_gate) {
                // TODO: add support for gated squared relu
                GGML_ABORT("fatal error: gated squared relu not implemented");
            } else {
@@ -814,7 +814,8 @@ struct llm_graph_context {
                   float   w_scale,
            llama_expert_gating_func_type gating_op,
                     int   il,
-             ggml_tensor * probs_in = nullptr) const;
+             ggml_tensor * probs_in = nullptr,
+             ggml_tensor * gate_up_exps = nullptr) const;

    ggml_tensor * build_moe_ffn(
             ggml_tensor * cur,
@@ -835,7 +836,9 @@ struct llm_graph_context {
                   float   w_scale,
            llama_expert_gating_func_type gating_op,
                     int   il,
-             ggml_tensor * probs_in = nullptr) const;
+             ggml_tensor * probs_in = nullptr,
+             ggml_tensor * gate_up_exps = nullptr,
+             ggml_tensor * gate_up_exps_b = nullptr) const;

    //
    // inputs
@@ -978,6 +978,9 @@ bool llama_kv_cache::get_can_shift() const {
    if (model.arch == LLM_ARCH_STEP35) {
        return false;
    }
+    if (hparams.n_pos_per_embd() > 1) {
+        return false;
+    }
    return true;
 }

@@ -979,6 +979,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                    type = LLM_TYPE_250M;
                }
            } break;
+        case LLM_ARCH_EUROBERT:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_CAUSAL,            hparams.causal_attn);
+                ml.get_key(LLM_KV_POOLING_TYPE,                hparams.pooling_type);
+
+                if (hparams.n_layer == 12) {
+                    type = LLM_TYPE_SMALL;  // 0.2B
+                }
+            } break;
        case LLM_ARCH_BLOOM:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -2970,6 +2980,15 @@ bool llama_model::load_tensors(llama_model_loader & ml) {

        // TODO: move to a separate function
        const auto tn = LLM_TN(arch);
+
+        // helper: try merged gate_up_exps first, fall back to separate gate and up
+        auto create_tensor_gate_up_exps = [&](llama_layer & layer, int bid, int64_t n_embd_, int64_t n_ff_, int64_t n_expert_, int flags) {
+            layer.ffn_gate_up_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS, "weight", bid), {n_embd_, n_ff_ * 2, n_expert_}, TENSOR_NOT_REQUIRED);
+            if (layer.ffn_gate_up_exps == nullptr) {
+                layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", bid), {n_embd_, n_ff_, n_expert_}, flags);
+                layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", bid), {n_embd_, n_ff_, n_expert_}, flags);
+            }
+        };
        switch (arch) {
            case LLM_ARCH_LLAMA:
            case LLM_ARCH_REFACT:
@@ -3570,6 +3589,29 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                    }
                } break;
+            case LLM_ARCH_EUROBERT:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                    }
+                } break;
            case LLM_ARCH_JINA_BERT_V2:
                {
                    tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
@@ -5188,9 +5230,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                            }

                            // MoE branch
-                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
-                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+                            create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);

                            // Shared expert branch
                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
@@ -7392,9 +7433,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                        }

                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+                        create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);

                        // Shared experts
                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
@@ -7458,9 +7498,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                        }

                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
-                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
-                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+                        create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);

                        // Shared experts
                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
@@ -8181,6 +8220,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
        case LLM_ARCH_NOMIC_BERT:
        case LLM_ARCH_NOMIC_BERT_MOE:
        case LLM_ARCH_NEO_BERT:
+        case LLM_ARCH_EUROBERT:
        case LLM_ARCH_WAVTOKENIZER_DEC:
        case LLM_ARCH_MODERN_BERT:
        case LLM_ARCH_GEMMA_EMBEDDING:
@@ -8378,6 +8418,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
            {
                llm = std::make_unique<llm_build_neo_bert>(*this, params);
            } break;
+        case LLM_ARCH_EUROBERT:
+            {
+                llm = std::make_unique<llm_build_eurobert>(*this, params);
+            } break;
        case LLM_ARCH_BLOOM:
            {
                llm = std::make_unique<llm_build_bloom>(*this, params);
@@ -9004,6 +9048,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_MODERN_BERT:
        case LLM_ARCH_NOMIC_BERT:
        case LLM_ARCH_NOMIC_BERT_MOE:
+        case LLM_ARCH_EUROBERT:
        case LLM_ARCH_STABLELM:
        case LLM_ARCH_BITNET:
        case LLM_ARCH_QWEN:
@@ -280,14 +280,16 @@ struct llama_layer {
    struct ggml_tensor * ffn_up_enc   = nullptr;

    // ff MoE
-    struct ggml_tensor * ffn_gate_inp    = nullptr;
-    struct ggml_tensor * ffn_gate_exps   = nullptr;
-    struct ggml_tensor * ffn_down_exps   = nullptr;
-    struct ggml_tensor * ffn_up_exps     = nullptr;
-    struct ggml_tensor * ffn_gate_inp_b  = nullptr;
-    struct ggml_tensor * ffn_gate_exps_b = nullptr;
-    struct ggml_tensor * ffn_down_exps_b = nullptr;
-    struct ggml_tensor * ffn_up_exps_b   = nullptr;
+    struct ggml_tensor * ffn_gate_inp      = nullptr;
+    struct ggml_tensor * ffn_gate_exps     = nullptr;
+    struct ggml_tensor * ffn_down_exps     = nullptr;
+    struct ggml_tensor * ffn_up_exps       = nullptr;
+    struct ggml_tensor * ffn_gate_up_exps  = nullptr;
+    struct ggml_tensor * ffn_gate_inp_b    = nullptr;
+    struct ggml_tensor * ffn_gate_exps_b   = nullptr;
+    struct ggml_tensor * ffn_down_exps_b   = nullptr;
+    struct ggml_tensor * ffn_up_exps_b     = nullptr;
+    struct ggml_tensor * ffn_gate_up_exps_b = nullptr;

    // ff shared expert (shexp)
    struct ggml_tensor * ffn_gate_inp_shexp = nullptr;
@@ -1890,7 +1890,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                    tokenizer_pre == "falcon-h1" ||
                    tokenizer_pre == "pixtral"  ||
                    tokenizer_pre == "midm-2.0" ||
-                    tokenizer_pre == "lfm2") {
+                    tokenizer_pre == "lfm2"     ||
+                    tokenizer_pre == "jina-v5-nano") {
                pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
                ignore_merges = true;
                add_bos = true;
@@ -218,7 +218,9 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
                LLM_FFN_SILU, hparams.expert_weights_norm,
                hparams.expert_weights_scale, hparams.expert_weights_scale,
                (llama_expert_gating_func_type) hparams.expert_gating_func,
-                il);
+                il,
+                nullptr,
+                model.layers[il].ffn_gate_up_exps);
            cb(moe_out, "ffn_moe_out", il);

            // FFN shared expert
@@ -0,0 +1,97 @@
+#include "models.h"
+
+llm_build_eurobert::llm_build_eurobert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    inpL = build_inp_embd(model.tok_embd);
+    cb(inpL, "inp_embd", -1);
+
+    auto * inp_attn = build_attn_inp_no_cache();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * cur = inpL;
+
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+
+        {
+            ggml_tensor * Qcur;
+            ggml_tensor * Kcur;
+            ggml_tensor * Vcur;
+
+            Qcur = build_lora_mm(model.layers[il].wq, cur);
+            Kcur = build_lora_mm(model.layers[il].wk, cur);
+            Vcur = build_lora_mm(model.layers[il].wv, cur);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                    );
+
+            Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, nullptr,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            cb(cur, "kqv_out", il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        cur = ggml_add(ctx0, cur, inpL);
+
+        ggml_tensor * ffn_inp = cur;
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up, NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_embd", -1);
+    res->t_embd = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
@@ -424,6 +424,10 @@ struct llm_build_neo_bert : public llm_graph_context {
    llm_build_neo_bert(const llama_model & model, const llm_graph_params & params);
 };

+struct llm_build_eurobert : public llm_graph_context {
+    llm_build_eurobert(const llama_model & model, const llm_graph_params & params);
+};
+
 template <bool iswa>
 struct llm_build_olmo2 : public llm_graph_context {
    llm_build_olmo2(const llama_model & model, const llm_graph_params & params);
@@ -380,7 +380,8 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int
            model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
            nullptr,
            n_expert, n_expert_used, LLM_FFN_SILU,
-            true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+            true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il,
+            nullptr, model.layers[il].ffn_gate_up_exps);
    cb(moe_out, "ffn_moe_out", il);

    // Add shared experts if present - following Qwen3Next reference implementation
@@ -479,7 +479,8 @@ ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const int
                model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
                nullptr,
                n_expert, n_expert_used, LLM_FFN_SILU,
-                true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+                true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il,
+                nullptr, model.layers[il].ffn_gate_up_exps);
        cb(moe_out, "ffn_moe_out", il);

        // Add shared experts if present - following Qwen3Next reference implementation
@@ -152,7 +152,7 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
    llama_build_and_test(test-grammar-parser.cpp)
    llama_build_and_test(test-grammar-integration.cpp)
    llama_build_and_test(test-llama-grammar.cpp)
-    llama_build_and_test(test-chat.cpp)
+    llama_build_and_test(test-chat.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
    # TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
    if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
        llama_build_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
@@ -13,7 +13,12 @@ fi
 name=$1
 input=$2

-make -j tests/test-tokenizer-0
+# Build using CMake if binary doesn't exist
+if [ ! -f ./build/bin/test-tokenizer-0 ]; then
+    printf "Building test-tokenizer-0 with CMake...\n"
+    cmake -B build -DLLAMA_BUILD_TESTS=ON
+    cmake --build build --target test-tokenizer-0 -j
+fi

 printf "Testing %s on %s ...\n" $name $input

@@ -23,7 +28,7 @@ printf "Tokenizing using (py)  Python AutoTokenizer ...\n"
 python3 ./tests/test-tokenizer-0.py ./models/tokenizers/$name --fname-tok $input > /tmp/test-tokenizer-0-$name-py.log 2>&1

 printf "Tokenizing using (cpp) llama.cpp ...\n"
-./tests/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1
+./build/bin/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1

 cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in"
 cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in"
@@ -57,8 +57,8 @@
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
-| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. Takes precedence over --mmap (default: enabled)<br/>(env: LLAMA_ARG_DIO) |
+| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)<br/>(env: LLAMA_ARG_DIO) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -109,14 +109,14 @@
 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
 | `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
-| `--temp N` | temperature (default: 0.80) |
+| `--temp, --temperature N` | temperature (default: 0.80) |
 | `--top-k N` | top-k sampling (default: 40, 0 = disabled)<br/>(env: LLAMA_ARG_TOP_K) |
 | `--top-p N` | top-p sampling (default: 0.95, 1.0 = disabled) |
 | `--min-p N` | min-p sampling (default: 0.05, 0.0 = disabled) |
-| `--top-nsigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) |
+| `--top-nsigma, --top-n-sigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) |
 | `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) |
 | `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) |
-| `--typical N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) |
+| `--typical, --typical-p N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) |
 | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
 | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) |
 | `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) |
@@ -140,8 +140,8 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
-| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. Takes precedence over --mmap (default: enabled)<br/>(env: LLAMA_ARG_DIO) |
+| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)<br/>(env: LLAMA_ARG_DIO) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -192,14 +192,14 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
 | `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
-| `--temp N` | temperature (default: 0.80) |
+| `--temp, --temperature N` | temperature (default: 0.80) |
 | `--top-k N` | top-k sampling (default: 40, 0 = disabled)<br/>(env: LLAMA_ARG_TOP_K) |
 | `--top-p N` | top-p sampling (default: 0.95, 1.0 = disabled) |
 | `--min-p N` | min-p sampling (default: 0.05, 0.0 = disabled) |
-| `--top-nsigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) |
+| `--top-nsigma, --top-n-sigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) |
 | `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) |
 | `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) |
-| `--typical N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) |
+| `--typical, --typical-p N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) |
 | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
 | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) |
 | `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) |
@@ -912,7 +912,9 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c

    const bool add_bos = llama_vocab_get_add_bos(vocab);

-    GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
+    if (llama_pooling_type(ctx) != LLAMA_POOLING_TYPE_LAST) {
+        GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
+    }

    auto tim1 = std::chrono::high_resolution_clock::now();
    LOG_INF("%s: tokenizing the input ..\n", __func__);
@@ -248,7 +248,7 @@ int32_t mtmd_helper_decode_image_chunk(

    int32_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk);
    int32_t i_batch = 0;
-    int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch;
+    int32_t n_img_batches = (n_tokens + n_batch - 1) / n_batch;
    decode_embd_batch batch_embd(encoded_embd, n_tokens, n_pos_per_embd, n_mmproj_embd);

    if (mtmd_decode_use_mrope(ctx)) {
@@ -74,8 +74,8 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model. Explicitly enabling mmap disables direct-io. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
-| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. Takes precedence over --mmap (default: enabled)<br/>(env: LLAMA_ARG_DIO) |
+| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)<br/>(env: LLAMA_ARG_DIO) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -126,14 +126,14 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
 | `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
-| `--temp N` | temperature (default: 0.80) |
+| `--temp, --temperature N` | temperature (default: 0.80) |
 | `--top-k N` | top-k sampling (default: 40, 0 = disabled)<br/>(env: LLAMA_ARG_TOP_K) |
 | `--top-p N` | top-p sampling (default: 0.95, 1.0 = disabled) |
 | `--min-p N` | min-p sampling (default: 0.05, 0.0 = disabled) |
-| `--top-nsigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) |
+| `--top-nsigma, --top-n-sigma N` | top-n-sigma sampling (default: -1.00, -1.0 = disabled) |
 | `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) |
 | `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) |
-| `--typical N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) |
+| `--typical, --typical-p N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) |
 | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
 | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) |
 | `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) |
@@ -162,9 +162,11 @@ For the full list of features, please refer to [server's changelog](https://gith

 | Argument | Explanation |
 | -------- | ----------- |
+| `-lcs, --lookup-cache-static FNAME` | path to static lookup cache to use for lookup decoding (not updated by generation) |
+| `-lcd, --lookup-cache-dynamic FNAME` | path to dynamic lookup cache to use for lookup decoding (updated by generation) |
 | `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_CTX_CHECKPOINTS) |
 | `-cram, --cache-ram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)<br/>(env: LLAMA_ARG_CACHE_RAM) |
-| `-kvu, --kv-unified` | use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
+| `-kvu, --kv-unified, -no-kvu, --no-kv-unified` | use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
 | `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
 | `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode |
 | `-sp, --special` | special tokens output enabled (default: false) |
@@ -182,7 +184,8 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-otd, --override-tensor-draft <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
 | `-cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model<br/>(env: LLAMA_ARG_CPU_MOE_DRAFT) |
 | `-ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model<br/>(env: LLAMA_ARG_N_CPU_MOE_DRAFT) |
-| `-a, --alias STRING` | set alias for model name (to be used by REST API)<br/>(env: LLAMA_ARG_ALIAS) |
+| `-a, --alias STRING` | set model name aliases, comma-separated (to be used by API)<br/>(env: LLAMA_ARG_ALIAS) |
+| `--tags STRING` | set model tags, comma-separated (informational, not used for routing)<br/>(env: LLAMA_ARG_TAGS) |
 | `--host HOST` | ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
 | `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
 | `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
@@ -229,6 +232,10 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: auto)<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
 | `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
 | `--spec-replace TARGET DRAFT` | translate the string in TARGET into DRAFT if the draft model and main model are not compatible |
+| `--spec-type [none\|ngram-cache\|ngram-simple\|ngram-map-k\|ngram-map-k4v\|ngram-mod]` | type of speculative decoding to use when no draft model is provided (default: none) |
+| `--spec-ngram-size-n N` | ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: 12) |
+| `--spec-ngram-size-m N` | ngram size M for ngram-simple/ngram-map speculative decoding, length of draft m-gram (default: 48) |
+| `--spec-ngram-min-hits N` | minimum hits for ngram-map speculative decoding (default: 1) |
 | `-mv, --model-vocoder FNAME` | vocoder model for audio generation (default: unused) |
 | `--tts-use-guide-tokens` | Use guide tokens to improve TTS word recall |
 | `--embd-gemma-default` | use default EmbeddingGemma model (note: can download weights from the internet) |
@@ -1510,7 +1517,7 @@ version = 1
 ; If the same key is defined in a specific preset, it will override the value in this global section.
 [*]
 c = 8192
-n-gpu-layer = 8
+n-gpu-layers = 8

 ; If the key corresponds to an existing model on the server,
 ; this will be used as the default config for that model
@@ -580,6 +580,8 @@ private:
    float slot_prompt_similarity = 0.0f;

    std::string model_name; // name of the loaded model, to be used by API
+    std::set<std::string> model_aliases; // additional names for the model
+    std::set<std::string> model_tags;    // informational tags

    bool sleeping = false;

@@ -813,10 +815,9 @@ private:
        SRV_WRN("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n");

        if (!params_base.model_alias.empty()) {
-            // user explicitly specified model name
-            model_name = params_base.model_alias;
+            // backward compat: use first alias as model name
+            model_name = *params_base.model_alias.begin();
        } else if (!params_base.model.name.empty()) {
-            // use model name in registry format (for models in cache)
            model_name = params_base.model.name;
        } else {
            // fallback: derive model name from file name
@@ -824,6 +825,9 @@ private:
            model_name = model_path.filename().string();
        }

+        model_aliases = params_base.model_alias;
+        model_tags    = params_base.model_tags;
+
        if (!is_resume) {
            return init();
        }
@@ -2363,7 +2367,7 @@ private:
                                            //printf("[DEBUG] `do_reset` was set to `true` after failing to restore a checkpoint");
                                        } else {
                                            pos_next = std::min(pos_next, std::max(it->pos_min + 1, it->pos_max));
-                                            n_past = slot.prompt.tokens.size_up_to_pos(pos_next);
+                                            n_past = std::min(slot.prompt.tokens.size_up_to_pos(pos_next), (size_t) it->n_tokens);
                                            SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, (float) checkpoint_size / 1024 / 1024);
                                        }
                                    }
@@ -2892,6 +2896,8 @@ server_context_meta server_context::get_meta() const {
    return server_context_meta {
        /* build_info             */ build_info,
        /* model_name             */ impl->model_name,
+        /* model_aliases          */ impl->model_aliases,
+        /* model_tags             */ impl->model_tags,
        /* model_path             */ impl->params_base.model.path,
        /* has_mtmd               */ impl->mctx != nullptr,
        /* has_inp_image          */ impl->chat_params.allow_image,
@@ -3688,6 +3694,8 @@ void server_routes::init_routes() {
            {"data", {
                {
                    {"id",       meta->model_name},
+                    {"aliases",  meta->model_aliases},
+                    {"tags",     meta->model_tags},
                    {"object",   "model"},
                    {"created",  std::time(0)},
                    {"owned_by", "llamacpp"},
@@ -1,3 +1,5 @@
+#pragma once
+
 #include "server-http.h"
 #include "server-task.h"
 #include "server-queue.h"
@@ -6,12 +8,15 @@

 #include <cstddef>
 #include <memory>
+#include <set>

 struct server_context_impl; // private implementation

 struct server_context_meta {
    std::string build_info;
    std::string model_name;
+    std::set<std::string> model_aliases;
+    std::set<std::string> model_tags;
    std::string model_path;
    bool has_mtmd;
    bool has_inp_image;
@@ -184,6 +184,51 @@ void server_models::add_model(server_model_meta && meta) {
    if (mapping.find(meta.name) != mapping.end()) {
        throw std::runtime_error(string_format("model '%s' appears multiple times", meta.name.c_str()));
    }
+
+    // check model name does not conflict with existing aliases
+    for (const auto & [key, inst] : mapping) {
+        if (inst.meta.aliases.count(meta.name)) {
+            throw std::runtime_error(string_format("model name '%s' conflicts with alias of model '%s'",
+                meta.name.c_str(), key.c_str()));
+        }
+    }
+
+    // parse aliases from preset's --alias option (comma-separated)
+    std::string alias_str;
+    if (meta.preset.get_option("LLAMA_ARG_ALIAS", alias_str) && !alias_str.empty()) {
+        for (auto & alias : string_split<std::string>(alias_str, ',')) {
+            alias = string_strip(alias);
+            if (!alias.empty()) {
+                meta.aliases.insert(alias);
+            }
+        }
+    }
+
+    // parse tags from preset's --tags option (comma-separated)
+    std::string tags_str;
+    if (meta.preset.get_option("LLAMA_ARG_TAGS", tags_str) && !tags_str.empty()) {
+        for (auto & tag : string_split<std::string>(tags_str, ',')) {
+            tag = string_strip(tag);
+            if (!tag.empty()) {
+                meta.tags.insert(tag);
+            }
+        }
+    }
+
+    // validate aliases do not conflict with existing names or aliases
+    for (const auto & alias : meta.aliases) {
+        if (mapping.find(alias) != mapping.end()) {
+            throw std::runtime_error(string_format("alias '%s' for model '%s' conflicts with existing model name",
+                alias.c_str(), meta.name.c_str()));
+        }
+        for (const auto & [key, inst] : mapping) {
+            if (inst.meta.aliases.count(alias)) {
+                throw std::runtime_error(string_format("alias '%s' for model '%s' conflicts with alias of model '%s'",
+                    alias.c_str(), meta.name.c_str(), key.c_str()));
+            }
+        }
+    }
+
    meta.update_args(ctx_preset, bin_path); // render args
    std::string name = meta.name;
    mapping[name] = instance_t{
@@ -249,6 +294,8 @@ void server_models::load_models() {
        server_model_meta meta{
            /* preset       */ preset.second,
            /* name         */ preset.first,
+            /* aliases      */ {},
+            /* tags         */ {},
            /* port         */ 0,
            /* status       */ SERVER_MODEL_STATUS_UNLOADED,
            /* last_used    */ 0,
@@ -265,10 +312,28 @@ void server_models::load_models() {
        for (const auto & [name, preset] : custom_presets) {
            custom_names.insert(name);
        }
+        auto join_set = [](const std::set<std::string> & s) {
+            std::string result;
+            for (const auto & v : s) {
+                if (!result.empty()) {
+                    result += ", ";
+                }
+                result += v;
+            }
+            return result;
+        };
+
        SRV_INF("Available models (%zu) (*: custom preset)\n", mapping.size());
        for (const auto & [name, inst] : mapping) {
            bool has_custom = custom_names.find(name) != custom_names.end();
-            SRV_INF("  %c %s\n", has_custom ? '*' : ' ', name.c_str());
+            std::string info;
+            if (!inst.meta.aliases.empty()) {
+                info += " (aliases: " + join_set(inst.meta.aliases) + ")";
+            }
+            if (!inst.meta.tags.empty()) {
+                info += " [tags: " + join_set(inst.meta.tags) + "]";
+            }
+            SRV_INF("  %c %s%s\n", has_custom ? '*' : ' ', name.c_str(), info.c_str());
        }
    }

@@ -291,7 +356,9 @@ void server_models::load_models() {
    for (const auto & [name, inst] : mapping) {
        std::string val;
        if (inst.meta.preset.get_option(COMMON_ARG_PRESET_LOAD_ON_STARTUP, val)) {
-            models_to_load.push_back(name);
+            if (common_arg_utils::is_truthy(val)) {
+                models_to_load.push_back(name);
+            }
        }
    }
    if ((int)models_to_load.size() > base_params.models_max) {
@@ -318,7 +385,15 @@ void server_models::update_meta(const std::string & name, const server_model_met

 bool server_models::has_model(const std::string & name) {
    std::lock_guard<std::mutex> lk(mutex);
-    return mapping.find(name) != mapping.end();
+    if (mapping.find(name) != mapping.end()) {
+        return true;
+    }
+    for (const auto & [key, inst] : mapping) {
+        if (inst.meta.aliases.count(name)) {
+            return true;
+        }
+    }
+    return false;
 }

 std::optional<server_model_meta> server_models::get_meta(const std::string & name) {
@@ -327,6 +402,11 @@ std::optional<server_model_meta> server_models::get_meta(const std::string & nam
    if (it != mapping.end()) {
        return it->second.meta;
    }
+    for (const auto & [key, inst] : mapping) {
+        if (inst.meta.aliases.count(name)) {
+            return inst.meta;
+        }
+    }
    return std::nullopt;
 }

@@ -764,7 +844,7 @@ static void res_err(std::unique_ptr<server_http_res> & res, const json & error_d
    res->data = safe_json_to_str({{ "error", error_data }});
 }

-static bool router_validate_model(const std::string & name, server_models & models, bool models_autoload, std::unique_ptr<server_http_res> & res) {
+static bool router_validate_model(std::string & name, server_models & models, bool models_autoload, std::unique_ptr<server_http_res> & res) {
    if (name.empty()) {
        res_err(res, format_error_response("model name is missing from the request", ERROR_TYPE_INVALID_REQUEST));
        return false;
@@ -774,6 +854,8 @@ static bool router_validate_model(const std::string & name, server_models & mode
        res_err(res, format_error_response(string_format("model '%s' not found", name.c_str()), ERROR_TYPE_INVALID_REQUEST));
        return false;
    }
+    // resolve alias to canonical model name
+    name = meta->name;
    if (models_autoload) {
        models.ensure_model_loaded(name);
    } else {
@@ -845,16 +927,16 @@ void server_models_routes::init_routes() {
        auto res = std::make_unique<server_http_res>();
        json body = json::parse(req.body);
        std::string name = json_value(body, "model", std::string());
-        auto model = models.get_meta(name);
-        if (!model.has_value()) {
+        auto meta = models.get_meta(name);
+        if (!meta.has_value()) {
            res_err(res, format_error_response("model is not found", ERROR_TYPE_NOT_FOUND));
            return res;
        }
-        if (model->status == SERVER_MODEL_STATUS_LOADED) {
+        if (meta->status == SERVER_MODEL_STATUS_LOADED) {
            res_err(res, format_error_response("model is already loaded", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }
-        models.load(name);
+        models.load(meta->name);
        res_ok(res, {{"success", true}});
        return res;
    };
@@ -875,6 +957,7 @@ void server_models_routes::init_routes() {
                preset_copy.unset_option("LLAMA_ARG_HOST");
                preset_copy.unset_option("LLAMA_ARG_PORT");
                preset_copy.unset_option("LLAMA_ARG_ALIAS");
+                preset_copy.unset_option("LLAMA_ARG_TAGS");
                status["preset"] = preset_copy.to_ini();
            }
            if (meta.is_failed()) {
@@ -883,6 +966,8 @@ void server_models_routes::init_routes() {
            }
            models_json.push_back(json {
                {"id",       meta.name},
+                {"aliases",  meta.aliases},
+                {"tags",     meta.tags},
                {"object",   "model"},    // for OAI-compat
                {"owned_by", "llamacpp"}, // for OAI-compat
                {"created",  t},          // for OAI-compat
@@ -910,7 +995,7 @@ void server_models_routes::init_routes() {
            res_err(res, format_error_response("model is not loaded", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }
-        models.unload(name);
+        models.unload(model->name);
        res_ok(res, {{"success", true}});
        return res;
    };
@@ -52,6 +52,8 @@ static std::string server_model_status_to_string(server_model_status status) {
 struct server_model_meta {
    common_preset preset;
    std::string name;
+    std::set<std::string> aliases; // additional names that resolve to this model
+    std::set<std::string> tags;    // informational tags, not used for routing
    int port = 0;
    server_model_status status = SERVER_MODEL_STATUS_UNLOADED;
    int64_t last_used = 0; // for LRU unloading
@@ -92,7 +92,7 @@ int main(int argc, char ** argv) {

    // for consistency between server router mode and single-model mode, we set the same model name as alias
    if (params.model_alias.empty() && !params.model.name.empty()) {
-        params.model_alias = params.model.name;
+        params.model_alias.insert(params.model.name);
    }

    common_init();
@@ -178,6 +178,7 @@ int main(int argc, char ** argv) {
    ctx_http.post("/v1/chat/completions", ex_wrapper(routes.post_chat_completions));
    ctx_http.post("/api/chat",            ex_wrapper(routes.post_chat_completions)); // ollama specific endpoint
    ctx_http.post("/v1/responses",        ex_wrapper(routes.post_responses_oai));
+    ctx_http.post("/responses",           ex_wrapper(routes.post_responses_oai));
    ctx_http.post("/v1/messages",         ex_wrapper(routes.post_anthropic_messages)); // anthropic messages API
    ctx_http.post("/v1/messages/count_tokens", ex_wrapper(routes.post_anthropic_count_tokens)); // anthropic token counting
    ctx_http.post("/infill",              ex_wrapper(routes.post_infill));
@@ -94,3 +94,20 @@ def test_no_webui():
    server.start()
    res = requests.get(url)
    assert res.status_code == 404
+
+
+def test_server_model_aliases_and_tags():
+    global server
+    server.model_alias = "tinyllama-2,fim,code"
+    server.model_tags = "chat,fim,small"
+    server.start()
+    res = server.make_request("GET", "/models")
+    assert res.status_code == 200
+    assert len(res.body["data"]) == 1
+    model = res.body["data"][0]
+    # aliases field must contain all aliases
+    assert set(model["aliases"]) == {"tinyllama-2", "fim", "code"}
+    # tags field must contain all tags
+    assert set(model["tags"]) == {"chat", "fim", "small"}
+    # id is derived from first alias (alphabetical order from std::set)
+    assert model["id"] == "code"
@@ -56,6 +56,7 @@ class ServerProcess:

    # custom options
    model_alias: str | None = None
+    model_tags: str | None = None
    model_url: str | None = None
    model_file: str | None = None
    model_draft: str | None = None
@@ -180,6 +181,8 @@ class ServerProcess:
            server_args.extend(["--pooling", self.pooling])
        if self.model_alias:
            server_args.extend(["--alias", self.model_alias])
+        if self.model_tags:
+            server_args.extend(["--tags", self.model_tags])
        if self.n_ctx:
            server_args.extend(["--ctx-size", self.n_ctx])
        if self.n_slots:
Author	SHA1	Message	Date
Jayant Lohia	ecbcb7ea9d	CUDA: add CDNA3 MFMA support for flash attention MMA kernel (#19806 ) * CUDA: add CDNA3 MFMA support for flash attention MMA kernel Add MI300X (gfx942) MFMA tensor core flash attention using v_mfma_f32_16x16x16_f16 (FP16 in, FP32 accumulate). - Add FATTN_WARP_SIZE=64 for CDNA wavefront64 - Add CDNA config for head sizes 64, 80, 96, 112, 128 - Add FP16 MFMA intrinsic path in mma.cuh - Add manual V transpose load for MFMA register layout - Route CDNA to MMA for prompt processing, VEC for token generation - Fix Q loading and combine stride granularity for non-power-of-2 heads Benchmarks (Qwen2.5-1.5B Q4_K_M, MI300X): pp512 +7%, pp1024 +13%, pp2048 +23%, pp4096 +39% tg128 -10% (FA overhead, VEC used for both) All 2480 flash attention tests pass. Ref: https://github.com/ggml-org/llama.cpp/issues/17917 * address review: replace FATTN_WARP_SIZE with constexpr, improve dispatch - Replace #define FATTN_WARP_SIZE with constexpr int warp_size = ggml_cuda_get_physical_warp_size() in each device function - Use ne[1]gqa_ratio threshold for MMA vs tile dispatch. Benchmarked crossover on MI300X @ d32768 with power-of-2 GQA models: hsk=64 (Llama 1B, gqa=4): MMA wins at eff >= 128 (+11%) hsk=128 (Llama 3B, gqa=4): MMA wins at eff >= 128 (+4%) Unified threshold: eff_nq >= 128 for all head sizes. - Remove VEC fallback; small batches fall through to tile kernel Update ggml/src/ggml-cuda/fattn.cu * use ggml_cuda_info().devices warp_size instead of hardcoded check --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>	2026-02-27 19:37:26 +01:00
Roj234	3e6ab244ad	server: Add pragma once to server-context.h (#19944 )	2026-02-27 18:28:36 +01:00
Sami Kama	5596a35791	server: Mirroring /v1/responses to /responses to match /v1/chat/completions pattern (#19873 )	2026-02-28 00:44:42 +08:00
Daniel Bevenius	8d3b962f47	ci : use ubuntu-latest for gguf-publish workflow (#19951 ) This commit changes the runner for the gguf-publish workflow from ubuntu-slim back to ubuntu-latest, which was updated in Commit `142cbe2ac6` ("ci : use new 1vCPU runner for lightweight jobs (#19107)"). The motivation for this is that the action used in the workflow depends on the docker daemon, which does not seem not available in the ubuntu-slim runner. This is currently causing an error in the workflow and preventing the gguf-publish workflow from running successfully. Today was the the first time since the original change (I think) that publish task has been run which may be why the issue was not noticed before. Refs: https://github.com/ggml-org/llama.cpp/actions/runs/22481900566	2026-02-27 14:42:24 +01:00
Aman Gupta	d903f30e25	ggml-cpu: add repack for mxfp4 (#19738 )	2026-02-27 18:15:09 +08:00
Daniel Bevenius	8387ffb28d	gguf-py : dump version to 0.18.0 (#19950 ) This commit updates the gguf-py package version to 0.18.0 in preperation of a new release to PyPI. Refs: https://github.com/ggml-org/llama.cpp/discussions/19948	2026-02-27 11:02:53 +01:00
Pascal	2e7e638523	server : support multiple model aliases via comma-separated --alias (#19926 ) * server : support multiple model aliases via comma-separated --alias * server : update --alias description and regenerate docs * server : multiple model aliases and tags - address review feedback from ngxson - --alias accepts comma-separated values (std::set, no duplicates) - --tags for informational metadata (not used for routing) - aliases resolve transparently in router via get_meta/has_model - /v1/models exposes aliases and tags fields * regenerate docs * nits * server : use first alias as model_name for backward compat address review feedback from ngxson * server : add single-model test for aliases and tags	2026-02-27 07:05:23 +01:00
Jan Patrick Lehr	a8b192b6ec	tests : enable test-chat out of tree build (#19558 ) The binary relies on model files that it tries to find. However, when configuring the build directory to be parallel to the source tree those heuristics fail. This sets the working directory for the test executable to be the source-tree which resolves this issue.	2026-02-27 05:37:54 +01:00
Neo Zhang	c17dce4f5c	replace the magic nunber 768 by max work group size to support iGPU (#19920 ) Co-authored-by: Neo Zhang Jianyu <jianyu.zhang@intel.com>	2026-02-27 09:26:07 +08:00
Vishal Singh	88cf781f51	ggml-zendnn: update code for latest ZenDNN API (#19923 ) - adapt ggml-zendnn.cpp to the new lowoha::matmul interface - update the ZenDNN git tag in CMake to the latest release (ZenDNN‑2026‑WW08) - add static lib support in CMake	2026-02-27 08:43:41 +08:00
Adrien Gallouët	4e76d24f28	ggml : fix AMX and add batched support (#19925 ) llama-perplexity -hf ggml-org/Qwen3-0.6B-GGUF:Q4_0 -f wikitext-2-raw/wiki.test.raw -c 2048 -b 2048 --chunks 2 before this commit: ``` perplexity: calculating perplexity over 2 chunks, n_ctx=2048, batch_size=2048, n_seq=1 perplexity: 2.31 seconds per pass - ETA 0.07 minutes [1]17.3868,[2]22.2199, Final estimate: PPL = 22.2199 +/- 1.59692 llama_perf_context_print: load time = 878.56 ms llama_perf_context_print: prompt eval time = 2037.82 ms / 4096 tokens ( 0.50 ms per token, 2009.99 tokens per second) llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) llama_perf_context_print: total time = 6403.17 ms / 4097 tokens llama_perf_context_print: graphs reused = 0 llama_memory_breakdown_print: \| memory breakdown [MiB] \| total free self model context compute unaccounted \| llama_memory_breakdown_print: \| - Host \| 845 = 318 + 224 + 302 \| llama_memory_breakdown_print: \| - CPU_REPACK \| 288 = 288 + 0 + 0 \| llama_memory_breakdown_print: \| - AMX \| 31 = 31 + 0 + 0 \| ``` after this commit: ``` perplexity: calculating perplexity over 2 chunks, n_ctx=2048, batch_size=2048, n_seq=1 perplexity: 1.98 seconds per pass - ETA 0.05 minutes [1]17.2005,[2]21.8220, Final estimate: PPL = 21.8220 +/- 1.56485 llama_perf_context_print: load time = 719.23 ms llama_perf_context_print: prompt eval time = 1676.23 ms / 4096 tokens ( 0.41 ms per token, 2443.58 tokens per second) llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) llama_perf_context_print: total time = 4258.74 ms / 4097 tokens llama_perf_context_print: graphs reused = 0 llama_memory_breakdown_print: \| memory breakdown [MiB] \| total free self model context compute unaccounted \| llama_memory_breakdown_print: \| - Host \| 845 = 318 + 224 + 302 \| llama_memory_breakdown_print: \| - AMX \| 319 = 319 + 0 + 0 \| ``` (no more CPU_REPACK) after this commit, disabling amx: ``` perplexity: calculating perplexity over 2 chunks, n_ctx=2048, batch_size=2048, n_seq=1 perplexity: 2.34 seconds per pass - ETA 0.07 minutes [1]17.2005,[2]21.8220, Final estimate: PPL = 21.8220 +/- 1.56485 llama_perf_context_print: load time = 841.91 ms llama_perf_context_print: prompt eval time = 2057.28 ms / 4096 tokens ( 0.50 ms per token, 1990.98 tokens per second) llama_perf_context_print: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second) llama_perf_context_print: total time = 6454.51 ms / 4097 tokens llama_perf_context_print: graphs reused = 0 llama_memory_breakdown_print: \| memory breakdown [MiB] \| total free self model context compute unaccounted \| llama_memory_breakdown_print: \| - Host \| 845 = 318 + 224 + 302 \| llama_memory_breakdown_print: \| - CPU_REPACK \| 319 = 319 + 0 + 0 \| ``` => same perplexity. Signed-off-by: Adrien Gallouët <angt@huggingface.co>	2026-02-26 21:39:11 +01:00
Ruben Ortlam	723c71064d	vulkan: fix fp16 Flash Attention on Windows AMD RDNA2 and below (#19921 )	2026-02-26 19:11:04 +01:00
Georgi Gerganov	37964f44f9	mtmd : fix padding of n_tokens (#19930 )	2026-02-26 18:39:49 +02:00
Georgi Gerganov	01cd448b8c	server : fix ctx checkpoint restore logic (#19924 )	2026-02-26 18:20:16 +02:00
Georgi Gerganov	99bd67c9b2	kv-cache : fix can_shift() check to take into account M-RoPE (#19928 )	2026-02-26 18:08:54 +02:00
Aman Gupta	b68d75165a	llama: Add option to merge gate and exp weights (#19139 ) * llama: Add option to merge gate and exp weights * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * update constants.py * add gate_up for the all MoE models * convert: simplify merge tensor condition * update constants.py * reduce number of models, add create_tensor_gate_up helper --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>	2026-02-26 21:01:08 +08:00
Kevin Pouget	ffaafde16f	ggml-virtgpu: improve the reliability of the code (#19846 ) * ggml-virtgpu-backend: validate the consistency of the received objects This patch adds consistency checks in the ggml-virtgpu-backend (running on the host side) to ensure that the data received from the guest is consistent (valid pointers, valid sizes and offsets). * ggml-virtgpu-backend: add fallback/skips for optional ggml backend methods ``` 1. bck->iface.synchronize(bck) 2. buft->iface.get_alloc_size(buft, op) 3. buft->iface.get_max_size(buft) ``` these three methods are optional in the GGML interface. `get_max_size` was already properly defaulted, but `backend sychronize` and `butf get_max_size` would have segfaulted the backend if not implemented. * ggml-virtgpu-backend: fix log format missing argument * ggml-virtgpu-backend: improve the abort message * ggml-virtgpu-backend: more safety checks * ggml-virtgpu-backend: new error code * ggml-virtgpu-backend: initialize all the error codes * ggml-virtgpu: add a missing comment generated by the code generator * ggml-virtgpu: add the '[virtgpu]' prefix to the device/buffer names * ggml-virtgpu: apir_device_buffer_from_ptr: improve the error message * ggml-virtgpu: shared: make it match the latest api_remoting.h of Virglrenderer APIR (still unmerged) * ggml-virtgpu: update the code generator to have dispatch_command_name in a host/guest shared file * ggml-virtgpu: REMOTE_CALL: fail if the backend returns an error * docs/backend/VirtGPU.md: indicate that the RAM+VRAM size is limed to 64 GB with libkrun * ggml-virtgpu: turn off clang-format header ordering for some of the files Compilation breaks when ordered alphabetically. * ggml-virtgpu: clang-format * ggml-virtgpu/backend/shared/api_remoting: better comments for the APIR return codes	2026-02-26 20:00:57 +08:00
drrros	efba35a860	server: fix load-on-startup not respected in ini file (#19897 ) Co-authored-by: Roman Marchenko <r.marchenko@ideco.ru>	2026-02-26 12:32:31 +01:00
Eric Zhang	9b62913b40	jinja : correct default size for string slices (#19913 )	2026-02-26 12:28:09 +01:00
Maximilian Werk	66287bdaac	model : add Jina Embeddings v5 Nano (partial EuroBERT) support (#19826 ) * WIP: Add EuroBERT support with autoformatting changes This commit includes: - EuroBERT model implementation for GGUF conversion - C++ backend support for EuroBERT architecture - Unintended autoformatting changes to Python files Saving before reverting formatting-only changes. * feat: add back eos assert when not last token pooling * feat: removed duplicated code and cleanup * feat: removed not working architectures and unnecessary check * fix: typo * fix: dynamic pooling config * feat: added an example model for eurobert * feat: proper llama-vocab implementation for jina-v5 * fix: removed unnecessary comments	2026-02-26 12:14:09 +01:00
Georgi Gerganov	1ca3d1de15	gguf : avoid too many file size calls (#19919 )	2026-02-26 12:46:32 +02:00
yggdrasil75	bd72300591	server : fix typo in server README.md (#19900 ) fix typo	2026-02-26 11:26:16 +01:00
Neo Zhang	2943210c1e	support permuted, remove check s0/s10 (#19889 ) Co-authored-by: Neo Zhang Jianyu <jianyu.zhang@intel.com>	2026-02-26 10:27:20 +08:00